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: "studying the impact of negotiation environments on negotiation teams performance"
  Abstract: In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions.

Connected through hyperedge HE-1 (induced by source paper "can we reach pareto optimal outcomes using bottom up approaches"):
  with members:
    - "unanimously acceptable agreements for negotiation teams in unpredictable domains"
    - "tasks for agent based negotiation teams analysis review and challenges"
    - "evolutionary aided negotiation model for bilateral bargaining in ambient intelligence domains with complex utility functions"
    - "achieving fully proportional representation is easy in practice"
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"
    - "techniques for highly multiobjective optimisation some nondominated points are better than others"

Connected through hyperedge HE-2 (induced by source paper "intra team strategies for teams negotiating against competitor matchers and conceders"):
  with members:
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"

Connected through hyperedge HE-3 (induced by source paper "tasks for agent based negotiation teams analysis review and challenges"):
  with members:
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"
    - "an anytime algorithm for optimal coalition structure generation"
    - "the strategy proofness landscape of merging"

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)