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: "generative multi agent behavioral cloning"
  Abstract: We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative, i.e., non-deterministic, multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces by introducing a hierarchy with macro-intent variables that encode long-term intent. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

Connected through hyperedge HE-1 (induced by source paper "stochastic prediction of multi agent interactions from partial observations"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "graph networks as learnable physics engines for inference and control"
    - "stochastic adversarial video prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "an algorithmic perspective on imitation learning"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-3 (induced by source paper "improved structural discovery and representation learning of multi agent data"):
  with members:
    - "google research football a novel reinforcement learning environment"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "learning 3d human dynamics from video"
    - "exploiting temporal information for 3d human pose estimation"
    - "deeppermnet visual permutation learning"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "neural message passing for quantum chemistry"
    - "coordinated multi agent imitation learning"

Connected through hyperedge HE-4 (induced by source paper "clone swarms learning to predict and control multi robot systems by imitation"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "primal pathfinding via reinforcement and imitation multi agent learning"
    - "relational inductive biases deep learning and graph networks"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "guided deep reinforcement learning for swarm systems"

Connected through hyperedge HE-5 (induced by source paper "learning from demonstration in the wild"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "deep cosine metric learning for person re identification"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "yolov3 an incremental improvement"
    - "sfv reinforcement learning of physical skills from videos"
    - "multi agent generative adversarial imitation learning"
    - "generating multi agent trajectories using programmatic weak supervision"

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.LG(Machine Learning)