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: "bnb adopt an asynchronous branch and bound dcop algorithm"
  Abstract: Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, and Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.

Connected through hyperedge HE-1 (induced by source paper "rmb dpop refining mb dpop by reducing redundant inferences"):
  with members:
    - "hs cai a hybrid dcop algorithm via combining search with context based inference"
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-2 (induced by source paper "structural self adaptation for decentralized pervasive intelligence"):
  with members:
    - "holarchic structures for decentralized deep learning a performance analysis"
    - "socio technical smart grid optimization via decentralized charge control of electric vehicles"
    - "self regulating supply demand systems"
    - "on the difficulty of training recurrent neural networks"
    - "improving the performance of k means for color quantization"

Connected through hyperedge HE-3 (induced by source paper "pt isabb a hybrid tree based complete algorithm to solve asymmetric distributed constraint optimization problems"):
  with members:
    - "asymmetric distributed constraint optimization problems"
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-4 (induced by source paper "asymdpop complete inference for asymmetric distributed constraint optimization problems"):
  with members:
    - "pt isabb a hybrid tree based complete algorithm to solve asymmetric distributed constraint optimization problems"
    - "distributed constraint optimization problems and applications a survey"

Connected through hyperedge HE-5 (induced by source paper "aed an anytime evolutionary dcop algorithm"):
  with members:
    - "a particle swarm based algorithm for functional distributed constraint optimization problems"
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-6 (induced by source paper "a generic approach for accelerating belief propagation based dcop algorithms via a branch and bound technique"):
  with members:
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-7 (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"
    - "asymmetric distributed constraint optimization problems"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "accelerating exact and approximate inference for distributed discrete optimization with gpus"):
  with members:
    - "beem bucket elimination with external memory"

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)