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: "adaptive load balancing a study in multi agent learning"
  Abstract: We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.

Connected through hyperedge HE-1 (induced by source paper "online reinforcement learning for dynamic multimedia systems"):
  with members:
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "collective intelligence data routing and braess paradox"):
  with members:
    - "collective intelligence for control of distributed dynamical systems"
    - "using collective intelligence to route internet traffic"
    - "an introduction to collective intelligence"
    - "adaptivity in agent based routing for data networks"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (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"
    - "general principles of learning based multi agent systems"
    - "using collective intelligence to route internet traffic"
    - "anytime coalition structure generation with worst case guarantees"
    - "reinforcement learning a survey"
    - "a market oriented programming environment and its application to distributed multicommodity flow problems"

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.AI(Artificial Intelligence)