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 "learning sharing behaviors with arbitrary numbers of agents"):
  Abstract: We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents. The individual behavior models are weighted finite state transducers (WFSTs) with weights dynamically updated during interactions, and the multi-agent fusion model is a logistic regression classifier.  We test our models in a multi-agent tower-building environment, where a Q-learning agent learns to interact with rule-based agents. Our approach accurately models the underlying behavior patterns of the rule-based agents with accuracy ranging between 0.63 and 1.0 depending on the stochasticity of the other agent behaviors. In addition we show using KL-divergence that the model accurately captures the distribution of next actions when interacting with both a single agent (KL-divergence < 0.1) and with multiple agents (KL-divergence < 0.37). Finally, we demonstrate that our behavior model can be used by a Q-learning agent to take turns in an interactive turn-taking environment.

Includes members:
  Member "on convergence and optimality of best response learning with policy types in multiagent systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "data efficient deep reinforcement learning for dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "multi agent reinforcement learning in sequential social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "belief and truth in hypothesised behaviours":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving")
  Member "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "playing atari with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "bayes bluff opponent modelling in poker":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a reduction of imitation learning and structured prediction to no regret online learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

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)