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: "evolutionary aided negotiation model for bilateral bargaining in ambient intelligence domains with complex utility functions"
  Abstract: Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.

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"
    - "studying the impact of negotiation environments on negotiation teams performance"
    - "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 "strategies for utility maximization in social groups with preferential exploration"):
  with members:
    - "maximizing utility among selfish users in social groups"
    - "social optimum in social groups with give and take criterion"
    - "device to device data storage for mobile cellular systems"
    - "pure price of anarchy for generalized second price auction"

Connected through hyperedge HE-3 (induced by source paper "unanimously acceptable agreements for negotiation teams in unpredictable domains"):
  with members:
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"
    - "an anytime algorithm for optimal coalition structure generation"

Connected through hyperedge HE-4 (induced by source paper "studying the impact of negotiation environments on negotiation teams performance"):
  with members:
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"
    - "multi issue negotiation with deadlines"

Connected through hyperedge HE-5 (induced by source paper "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"):
  with members:
    - "multi issue negotiation with deadlines"

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)