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: "step spatial temporal graph convolutional networks for emotion perception from gaits"
  Abstract: We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of $2,177$ human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 89% on E-Gait, which is 14 - 30% more accurate over prior methods.

Connected through hyperedge HE-1 (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"
    - "context aware emotion recognition networks"
    - "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-2 (induced by source paper "take an emotion walk perceiving emotions from gaits using hierarchical attention pooling and affective mapping"):
  with members:
    - "context aware emotion recognition networks"
    - "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-3 (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"
    - "context aware emotion recognition networks"
    - "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)