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: "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
  Abstract: Emotion recognition is attracting great interest for its potential application in a multitude of real-life situations. Much of the Computer Vision research in this field has focused on relating emotions to facial expressions, with investigations rarely including more than upper body. In this work, we propose a new scenario, for which emotional states are related to 3D dynamics of the whole body motion. To address the complexity of human body movement, we used covariance descriptors of the sequence of the 3D skeleton joints, and represented them in the non-linear Riemannian manifold of Symmetric Positive Definite matrices. In doing so, we exploited geodesic distances and geometric means on the manifold to perform emotion classification. Using sequences of spontaneous walking under the five primary emotional states, we report a method that succeeded in classifying the different emotions, with comparable performance to those observed in a human-based force-choice classification task.

Connected through hyperedge HE-1 (induced by source paper "step spatial temporal graph convolutional networks for emotion perception from gaits"):
  with members:
    - "identifying emotions from walking using affective and deep features"
    - "video to video synthesis"
    - "pose guided human video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep video generation prediction and completion of human action sequences"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mocogan decomposing motion and content for video generation"

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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-4 (induced by source paper "identifying emotions from walking using affective and deep features"):
  with members:
    - "modeling data driven dominance traits for virtual characters using gait analysis"
    - "fva modeling perceived friendliness of virtual agents using movement characteristics"
    - "pose guided human video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep video generation prediction and completion of human action sequences"
    - "lstm a search space odyssey"
    - "lstm pose machines"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-5 (induced by source paper "eva generating emotional behavior of virtual agents using expressive features of gait and gaze"):
  with members:
    - "modeling data driven dominance traits for virtual characters using gait analysis"
    - "fva modeling perceived friendliness of virtual agents using movement characteristics"
    - "pedestrian dominance modeling for socially aware robot navigation"
    - "data driven modeling of group entitativity in virtual environments"
    - "learning 3d human pose from structure and motion"

Connected through hyperedge HE-6 (induced by source paper "a novel geometric framework on gram matrix trajectories for human behavior understanding"):
  with members:
    - "elastic functional coding of riemannian trajectories"
    - "kernelized covariance for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "shape distributions of nonlinear dynamical systems for video based inference"
    - "differential recurrent neural networks for 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)