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: "systems of bounded rational agents with information theoretic constraints"
  Abstract: Specialization and hierarchical organization are important features of efficient collaboration in economical, artificial, and biological systems. Here, we investigate the hypothesis that both features can be explained by the fact that each entity of such a system is limited in a certain way. We propose an information-theoretic approach based on a Free Energy principle, in order to computationally analyze systems of bounded rational agents that deal with such limitations optimally. We find that specialization allows to focus on fewer tasks, thus leading to a more efficient execution, but in turn requires coordination in hierarchical structures of specialized experts and coordinating units. Our results suggest that hierarchical architectures of specialized units at lower levels that are coordinated by units at higher levels are optimal, given that each unit's information-processing capability is limited and conforms to constraints on complexity costs.

Connected through hyperedge HE-1 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "an information theoretic on line update principle for perception action coupling"
    - "bounded rational decision making with adaptive neural network priors"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "planning with information processing constraints and model uncertainty in markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "bounded rational decision making from elementary computations that reduce uncertainty"):
  with members:
    - "bounded rational decision making with adaptive neural network priors"
    - "a short characterization of relative entropy"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "information measures the curious case of the binary alphabet"
    - "a minimum relative entropy principle for learning and acting"
    - "provably bounded optimal agents"

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.MA(Multiagent Systems)