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: "asymmetric distributed constraint optimization problems"
  Abstract: Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model.#R##N##R##N#The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated.#R##N##R##N#Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy.

Connected through hyperedge HE-1 (induced by source paper "pt isabb a hybrid tree based complete algorithm to solve asymmetric distributed constraint optimization problems"):
  with members:
    - "bnb adopt an asynchronous branch and bound dcop algorithm"
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-2 (induced by source paper "a review of literature on parallel constraint solving"):
  with members:
    - "parallel strategies selection"
    - "solving and verifying the boolean pythagorean triples problem via cube and conquer"
    - "a multicore tool for constraint solving"
    - "hordesat a massively parallel portfolio sat solver"
    - "the shape of the search tree for the maximum clique problem and the implications for parallel branch and bound"
    - "proteus a hierarchical portfolio of solvers and transformations"
    - "algorithm selection for combinatorial search problems a survey"
    - "distributed solving through model splitting"

Connected through hyperedge HE-3 (induced by source paper "distributed constraint optimization problems and applications a survey"):
  with members:
    - "accelerating exact and approximate inference for distributed discrete optimization with gpus"
    - "a realistic dataset for the smart home device scheduling problem for dcops"
    - "bnb adopt an asynchronous branch and bound dcop algorithm"
    - "regret based multi agent coordination with uncertain task rewards"
    - "graphical models for game theory"
    - "message passing algorithms for quadratic programming formulations of map estimation"
    - "asynchronous forward bounding for distributed cops"
    - "completeness and performance of the apo algorithm"

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)