human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "incremental clustering and expansion for faster optimal planning in dec pomdps"):
  Abstract: This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm, which reduces the problem to a tree of one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs that can be solved optimally.  First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality.  Second, we introduce incremental expansion of nodes in the GMAA* search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the node's depth.  This is particularly beneficial when little clustering is possible.  In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs.  We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent.  Finally, we present extensive empirical results demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can optimally solve Dec-POMDPs of unprecedented size.

Includes members:
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")
  Member "optimizing memory bounded controllers for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning for heterogeneous teams with palo bounds")
  Member "anytime planning for decentralized pomdps using expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning for multi robot cooperation in partially observable stochastic environments with macro actions")
  Member "rollout sampling policy iteration for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep decentralized multi task multi agent reinforcement learning under partial observability")
  Member "improved memory bounded dynamic programming for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on solving cooperative marl problems with a few good experiences")
  Member "maa a heuristic search algorithm for solving decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep actor critic reinforcement learning framework for dynamic multichannel access")
  Member "policy iteration for decentralized control of markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies")
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")

Question: Please tell me which class the source paper 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)