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: "how deep neural networks can improve emotion recognition on video data"
  Abstract: We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural network models such as recurrent neural networks (RNNs) on hand-crafted features, few works have considered combining convolutional neural networks (CNNs) with RNNs. In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance. We present our findings on videos from the Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the effects of several hyperparameters on overall performance while also achieving superior performance to the baseline and other competing methods.

Connected through hyperedge HE-1 (induced by source paper "audio video emotion recognition in the wild using deep hybrid networks"):
  with members:
    - "rethinking the inception architecture for computer vision"
    - "effective face frontalization in unconstrained images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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-3 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-4 (induced by source paper "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "audeering s approach to the one minute gradual emotion challenge"

Connected through hyperedge HE-5 (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-6 (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"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"

Connected through hyperedge HE-7 (induced by source paper "monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks"):
  with members:
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"
    - "tool and phase recognition using contextual cnn features"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "robust emotion recognition from low quality and low bit rate video a deep learning approach"):
  with members:
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "studying very low resolution recognition using deep networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "self tuned deep super resolution"
    - "learning super resolution jointly from external and internal examples"

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)