human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generating multi agent trajectories using programmatic weak supervision"):
  Abstract: We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime. 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.

Includes members:
  Member "robust imitation of diverse behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "coordinated multi agent imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "generating long term trajectories using deep hierarchical networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "learning the structure of generative models without labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "learning recurrent representations for hierarchical behavior modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning calibratable policies using programmatic style consistency")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)