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: "towards social artificial intelligence nonverbal social signal prediction in a triadic interaction"
  Abstract: We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use. This research direction is essential to make a machine that genuinely communicates with humans, which we call Social Artificial Intelligence. We first formulate the "social signal prediction" problem as a way to model the dynamics of social signals exchanged among interacting individuals in a data-driven way. We then present a new 3D motion capture dataset to explore this problem, where the broad spectrum of social signals (3D body, face, and hand motions) are captured in a triadic social interaction scenario. Baseline approaches to predict speaking status, social formation, and body gestures of interacting individuals are presented in the defined social prediction framework.

Connected through hyperedge HE-1 (induced by source paper "improving social awareness through dante a deep affinity network for clustering conversational interactants"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "conversational group detection with deep convolutional networks"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "humbi a large multiview dataset of human body expressions"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "self supervised adaptation of high fidelity face models for monocular performance tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep appearance models for face rendering"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

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)