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: "negotiating socially optimal allocations of resources"
  Abstract: A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory to assess the social welfare of such an agent society. In this paper, we study an abstract negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then analyse how these deals affect social welfare for different instances of the basic framework and different interpretations of the concept of social welfare itself. In particular, we show how certain classes of deals are both sufficient and necessary to guarantee that a socially optimal allocation of resources will be reached eventually.

Connected through hyperedge HE-1 (induced by source paper "why not give this work to them explaining ai moderated task allocation outcomes using negotiation trees"):
  with members:
    - "negotiation strategies for agents with ordinal preferences"
    - "towards providing explanations for ai planner decisions"
    - "explanation in artificial intelligence insights from the social sciences"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "multi cycle assignment problems with rotational diversity"):
  with members:
    - "rotational diversity in multi cycle assignment problems"
    - "reinforcement learning for automatic test case prioritization and selection in continuous integration"
    - "time aware test case execution scheduling for cyber physical systems"
    - "scheduling a dynamic aircraft repair shop with limited repair resources"
    - "robust local search for solving rcpsp max with durational uncertainty"
    - "bin completion algorithms for multicontainer packing knapsack and covering problems"

Connected through hyperedge HE-3 (induced by source paper "lecture notes on fair division"):
  with members:
    - "extremal behaviour in multiagent contract negotiation"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-4 (induced by source paper "endogenous games with goals side payments among goal directed agents"):
  with members:
    - "non cooperative games with preplay negotiations"
    - "a primer on strategic games"

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.MA(Multiagent Systems)