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: "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.

Connected through hyperedge HE-1 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "collaborative planning for mixed autonomy lane merging"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "carla an open urban driving simulator"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-2 (induced by source paper "optimal decision making in mixed agent partially observable stochastic environments via reinforcement learning"):
  with members:
    - "the complexity of decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "near optimal adversarial policy switching for decentralized asynchronous multi agent systems"):
  with members:
    - "ssd single shot multibox detector"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "the complexity of decentralized control of markov decision processes"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-4 (induced by source paper "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"):
  with members:
    - "a framework for sequential planning in multi agent settings"

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)