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: "context aware emotion recognition networks"
  Abstract: Traditional techniques for emotion recognition have focused on the facial expression analysis only, thus providing limited ability to encode context that comprehensively represents the emotional responses. We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner. The key idea is to hide human faces in a visual scene and seek other contexts based on an attention mechanism. Our networks consist of two sub-networks, including two-stream encoding networks to seperately extract the features of face and context regions, and adaptive fusion networks to fuse such features in an adaptive fashion. We also introduce a novel benchmark for context-aware emotion recognition, called CAER, that is more appropriate than existing benchmarks both qualitatively and quantitatively. On several benchmarks, CAER-Net proves the effect of context for emotion recognition. Our dataset is available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "two stream aural visual affect analysis in the wild"):
  with members:
    - "multi label class balancing algorithm for action unit detection"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "fau facial expressions valence and arousal a multi task solution"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "retinaface single stage dense face localisation in the wild"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "arcface additive angular margin loss for deep face recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-2 (induced by source paper "emoticon context aware multimodal emotion recognition using frege s principle"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "identifying emotions from walking using affective and deep features"
    - "group emotion recognition using machine learning"
    - "image captioning with integrated bottom up and multi level residual top down attention for game scene understanding"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "take an emotion walk perceiving emotions from gaits using hierarchical attention pooling and affective mapping"):
  with members:
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "predicting 3d human dynamics from video"
    - "identifying emotions from walking using affective and deep features"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "pose guided human video generation"
    - "quaternet a quaternion based recurrent model for human motion"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "the liar s walk detecting deception with gait and gesture"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "gait recognition via disentangled representation learning"
    - "identifying emotions from walking using affective and deep features"
    - "pose guided human video generation"
    - "real world anomaly detection in surveillance videos"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

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)