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: "decentralized stochastic control with partial history sharing a common information approach"
  Abstract: A general model of decentralized stochastic control called partial history sharing information structure is presented. In this model, at each step the controllers share part of their observation and control history with each other. This general model subsumes several existing models of information sharing as special cases. Based on the information commonly known to all the controllers, the decentralized problem is reformulated as an equivalent centralized problem from the perspective of a coordinator. The coordinator knows the common information and select prescriptions that map each controller's local information to its control actions. The optimal control problem at the coordinator is shown to be a partially observable Markov decision process (POMDP) which is solved using techniques from Markov decision theory. This approach provides (a) structural results for optimal strategies, and (b) a dynamic program for obtaining optimal strategies for all controllers in the original decentralized problem. Thus, this approach unifies the various ad-hoc approaches taken in the literature. In addition, the structural results on optimal control strategies obtained by the proposed approach cannot be obtained by the existing generic approach (the person-by-person approach) for obtaining structural results in decentralized problems; and the dynamic program obtained by the proposed approach is simpler than that obtained by the existing generic approach (the designer's approach) for obtaining dynamic programs in decentralized problems.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-2 (induced by source paper "team optimal decentralized state estimation of linear stochastic processes by agents with non classical information structures"):
  with members:
    - "optimal control of two player systems with output feedback"
    - "optimal distributed controller design with communication delays application to vehicle formations"

Connected through hyperedge HE-3 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "active sequential hypothesis testing"

Connected through hyperedge HE-4 (induced by source paper "simplified action decoder for deep multi agent reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "re determinizing information set monte carlo tree search in hanabi"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "distributed prioritized experience replay"
    - "evaluating and modelling hanabi playing agents"

Connected through hyperedge HE-6 (induced by source paper "non cooperative inverse reinforcement learning"):
  with members:
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "option critic in cooperative multi agent systems"):
  with members:
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "the option critic architecture"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "adam a method for stochastic optimization"
    - "learning to cooperate via policy search"
    - "generating sequences with recurrent neural networks"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-8 (induced by source paper "learning from my partner s actions roles in decentralized robot teams"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "fully decentralized policies for multi agent systems an information theoretic approach"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "cooperative inverse reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "optimal control of two player systems with output feedback"
    - "planning for decentralized control of multiple robots under uncertainty"

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.SY(Systems and Control)