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: "dante deep affinity network for clustering conversational interactants"
  Abstract: We propose a data-driven approach to visually detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering contextual information like the position and orientation of other nearby individuals. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and bigger groups. The results from our evaluation on two standard benchmarks suggest that the combination of powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Our technique has a wide range of applications from visual scene understanding, e.g., for surveillance, to social robotics.

Connected through hyperedge HE-1 (induced by source paper "mgpi a computational model of multiagent group perception and interaction"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "f formation detection individuating free standing conversational groups in images"

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)