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: "distributed constraint optimization problems and applications a survey"
  Abstract: The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments.#N##N#This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

Connected through hyperedge HE-1 (induced by source paper "hs cai a hybrid dcop algorithm via combining search with context based inference"):
  with members:
    - "pt isabb a hybrid tree based complete algorithm to solve asymmetric distributed constraint optimization problems"
    - "asynchronous forward bounding for distributed cops"

Connected through hyperedge HE-2 (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"
    - "bnb adopt an asynchronous branch and bound dcop algorithm"

Connected through hyperedge HE-3 (induced by source paper "the sharer s dilemma in collective adaptive systems of self interested agents"):
  with members:
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "machine theory of mind"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "recycled admm improve privacy and accuracy with less computation in distributed algorithms"):
  with members:
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "adaptive admm with spectral penalty parameter selection"
    - "distributed event localization via alternating direction method of multipliers"
    - "differentially private distributed optimization"

Connected through hyperedge HE-5 (induced by source paper "a realistic dataset for the smart home device scheduling problem for dcops"):
  with members:
    - "bnb adopt an asynchronous branch and bound dcop algorithm"
    - "coalition structure generation over graphs"
    - "asynchronous forward bounding for distributed cops"

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)