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: "emotionet challenge recognition of facial expressions of emotion in the wild"
  Abstract: This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis of a large number of images of facial expressions of emotion in the wild. The challenge was divided into two tracks. The first track tested the ability of current computer vision algorithms in the automatic detection of action units (AUs). Specifically, we tested the detection of 11 AUs. The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions. Specifically, we tested the recognition of 16 basic and compound emotion categories. The results of the challenge suggest that current computer vision and machine learning algorithms are unable to reliably solve these two tasks. The limitations of current algorithms are more apparent when trying to recognize emotion. We also show that current algorithms are not affected by mild resolution changes, small occluders, gender or age, but that 3D pose is a major limiting factor on performance. We provide an in-depth discussion of the points that need special attention moving forward.

Connected through hyperedge HE-1 (induced by source paper "multitask emotion recognition with incomplete labels"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "mimamo net integrating micro and macro motion for video emotion recognition"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "the omg emotion behavior dataset"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-2 (induced by source paper "face behavior a la carte expressions affect and action units in a single network"):
  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"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "two level attention with two stage multi task learning for facial emotion recognition"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"

Connected through hyperedge HE-3 (induced by source paper "a facial affect analysis system for autism spectrum disorder"):
  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"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "deep facial expression recognition a survey"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "cake compact and accurate k dimensional representation of emotion"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-4 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep covariance descriptors for facial expression recognition"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "covariance pooling for facial expression recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"

Connected through hyperedge HE-5 (induced by source paper "an occam s razor view on learning audiovisual emotion recognition with small training sets"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "covariance pooling for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "learnable pooling with context gating for video classification"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "improved regularization of convolutional neural networks with cutout"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-6 (induced by source paper "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"):
  with members:
    - "a survey on multi task learning"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "identity mappings in deep residual networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "automatic facial expression recognition using features of salient facial patches"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "facial expression analysis under partial occlusion a survey"):
  with members:
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "confidence weighted local expression predictions for occlusion handling in expression recognition and action unit detection"
    - "face expression recognition and analysis the state of the art"

Connected through hyperedge HE-8 (induced by source paper "temporal multimodal fusion for video emotion classification in the wild"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "spatiotemporal networks for video emotion recognition"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "audio visual emotion recognition with temporal alignment and perception attention"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "moddrop adaptive multi modal gesture recognition"
    - "two stream convolutional networks for action recognition in videos"

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)