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: "general principles of learning based multi agent systems"
  Abstract: We consider the problem of how to design large decentralized multi-agent systems (MAS's) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into one of how to automatically set/update the reward functions for each of the agents so that the global goal is achieved. In particular we do not want the agents to ``work at cross-purposes'' as far as the global goal is concerned. We use the term artificial COllective INtelligence (COIN) to refer to systems that embody solutions to this problem. In this paper we present a summary of a mathematical framework for COINs. We then investigate the real-world applicability of the core concepts of that framework via two computer experiments: we show that our COINs perform near optimally in a difficult variant of Arthur's bar problem (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINs in the leader-follower problem.

Connected through hyperedge HE-1 (induced by source paper "communication efficient distributed reinforcement learning"):
  with members:
    - "dynamic power distribution system management with a locally connected communication network"
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "stochastic variance reduced policy gradient"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "proximal policy optimization algorithms"
    - "a berkeley view of systems challenges for ai"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "avoiding braess paradox through collective intelligence"):
  with members:
    - "using collective intelligence to route internet traffic"
    - "an introduction to collective intelligence"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "communication based decomposition mechanisms for decentralized mdps"):
  with members:
    - "learning to cooperate via policy search"
    - "distributed planning in hierarchical factored mdps"
    - "decentralized control of cooperative systems categorization and complexity analysis"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-4 (induced by source paper "decentralized control of cooperative systems categorization and complexity analysis"):
  with members:
    - "learning to cooperate via policy search"
    - "distributed planning in hierarchical factored mdps"
    - "efficient solution algorithms for factored mdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-5 (induced by source paper "an introduction to collective intelligence"):
  with members:
    - "learning conventions in multiagent stochastic domains using likelihood estimates"
    - "avoiding braess paradox through collective intelligence"
    - "using collective intelligence to route internet traffic"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"
    - "adaptive load balancing a study in multi agent learning"
    - "a market oriented programming environment and its application to distributed multicommodity flow problems"

Connected through hyperedge HE-6 (induced by source paper "adaptivity in agent based routing for data networks"):
  with members:
    - "avoiding braess paradox through collective intelligence"
    - "using collective intelligence to route internet traffic"
    - "an introduction to collective intelligence"

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)