human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "avec 2016 depression mood and emotion recognition workshop and challenge"
  Abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) “Depression, Mood and Emotion” will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the ∗The author is further affiliated with Imperial College London, Department of Computing, London, U.K. †The author is further affiliated with University of Passau, Chair of Complex & Intelligent Systems ‡The author is further affiliated with Twente University, EEMCS, Twente, The Netherlands. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. AVEC’16 16 October 2016, Amsterdam, NL Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3119-7/14/11. . . $15.00. http://dx.doi.org/10.1145/2661806.2661807 . challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.

Connected through hyperedge HE-1 (induced by source paper "deep learning for human affect recognition insights and new developments"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "regularization for deep learning a taxonomy"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "attention is all you need"
    - "understanding hidden memories of recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "inferring dynamic representations of facial actions from a still image"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a functional regression approach to facial landmark tracking"
    - "action anticipation by predicting future dynamic images"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

Connected through hyperedge HE-3 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "the omg emotion behavior dataset"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "multimodal generative models for scalable weakly supervised learning"

Connected through hyperedge HE-4 (induced by source paper "jointly aligning and predicting continuous emotion annotations"):
  with members:
    - "trainable time warping aligning time series in the continuous time domain"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-5 (induced by source paper "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "the omg emotion behavior dataset"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "soundnet learning sound representations from unlabeled video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "bounded residual gradient networks breg net for facial affect computing"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "facial expression recognition from world wild web"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning grimaces by watching tv"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "attending to emotional narratives"):
  with members:
    - "modeling emotion in complex stories the stanford emotional narratives dataset"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "memory fusion network for multi view sequential learning"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "how deep neural networks can improve emotion recognition on video data"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)