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 bilinear programming approach for multiagent planning"
  Abstract: Multiagent planning and coordination problems are common and known to be computationally hard. We show that a wide range of two-agent problems can be formulated as bilinear programs. We present a successive approximation algorithm that significantly outperforms the coverage set algorithm, which is the state-of-the-art method for this class of multiagent problems. Because the algorithm is formulated for bilinear programs, it is more general and simpler to implement. The new algorithm can be terminated at any time and-unlike the coverage set algorithm-it facilitates the derivation of a useful online performance bound. It is also much more efficient, on average reducing the computation time of the optimal solution by about four orders of magnitude. Finally, we introduce an automatic dimensionality reduction method that improves the effectiveness of the algorithm, extending its applicability to new domains and providing a new way to analyze a subclass of bilinear programs.

Connected through hyperedge HE-1 (induced by source paper "a sufficient statistic for influence in structured multiagent environments"):
  with members:
    - "near optimal behavior via approximate state abstraction"
    - "tractable inference for complex stochastic processes"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "scaling up decentralized mdps through heuristic search"):
  with members:
    - "an investigation into mathematical programming for finite horizon decentralized pomdps"
    - "policy iteration for decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "decentralized control of cooperative systems categorization and complexity analysis"

Connected through hyperedge HE-3 (induced by source paper "an investigation into mathematical programming for finite horizon decentralized pomdps"):
  with members:
    - "optimizing memory bounded controllers for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

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)