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: "modeling data driven dominance traits for virtual characters using gait analysis"
  Abstract: We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ~73% accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.

Connected through hyperedge HE-1 (induced by source paper "identifying emotions from walking using affective and deep features"):
  with members:
    - "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"
    - "learning 3d human pose from structure and motion"

Connected through hyperedge HE-2 (induced by source paper "eva generating emotional behavior of virtual agents using expressive features of gait and gaze"):
  with members:
    - "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"
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"

Connected through hyperedge HE-3 (induced by source paper "pedestrian dominance modeling for socially aware robot navigation"):
  with members:
    - "the socially invisible robot navigation in the social world using robot entitativity"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "enabling robots to communicate their objectives"
    - "socially aware motion planning with deep reinforcement learning"

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.GR(Graphics)