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 "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"):
  Abstract: Recent advances in Bayesian reinforcement learning (BRL) have shown that Bayes-optimality is theoretically achievable by modeling the environment's latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In self-interested multi-agent environments, the transition dynamics are mainly controlled by the other agent's stochastic behavior for which FDM's independence and modeling assumptions do not hold. As a result, FDM does not allow the other agent's behavior to be generalized across different states nor specified using prior domain knowledge. To overcome these practical limitations of FDM, we propose a generalization of BRL to integrate the general class of parametric models and model priors, thus allowing practitioners' domain knowledge to be exploited to produce a fine-grained and compact representation of the other agent's behavior. Empirical evaluation shows that our approach outperforms existing multi-agent reinforcement learning algorithms.

Includes members:
  Member "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "informative path planning for mobile sensing with reinforcement learning")
  Member "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interactive decision making for autonomous vehicles in dense traffic")
  Member "near optimal brl using optimistic local transitions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian reinforcement learning via deep sparse sampling")
  Member "learning is planning near bayes optimal reinforcement learning via monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "varibad a very good method for bayes adaptive deep rl via meta learning")
  Member "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "decision theoretic coordination and control for active multi camera surveillance in uncertain partially observable environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gp localize persistent mobile robot localization using online sparse gaussian process observation model")
  Member "active markov information theoretic path planning for robotic environmental sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "implicit posterior variational inference for deep gaussian processes")

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)