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: "an anytime algorithm for optimal coalition structure generation"
  Abstract: Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques -- ranging from dynamic programming, to integer programming, to stochastic search -- all of which suffer from major limitations relating to execution time, solution quality, and memory requirements.#R##N##R##N#With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.

Connected through hyperedge HE-1 (induced by source paper "stable outcomes in modified fractional hedonic games"):
  with members:
    - "strategyproof mechanisms for additively separable hedonic games and fractional hedonic games"
    - "fractional hedonic games"
    - "mechanism design for team formation"
    - "simple causes of complexity in hedonic games"
    - "coalition structure generation over graphs"
    - "complexity of coalition structure generation"

Connected through hyperedge HE-2 (induced by source paper "an online mechanism for ridesharing in autonomous mobility on demand systems"):
  with members:
    - "the social dilemma of autonomous vehicles"
    - "managing autonomous mobility on demand systems for better passenger experience"
    - "incentive design for ridesharing with uncertainty"
    - "anytime heuristic search"

Connected through hyperedge HE-3 (induced by source paper "algorithms for graph constrained coalition formation in the real world"):
  with members:
    - "coalition structure generation over graphs"

Connected through hyperedge HE-4 (induced by source paper "cooperative games with overlapping coalitions charting the tractability frontier"):
  with members:
    - "on parameterized complexity of group activity selection problems on social networks"
    - "group activity selection on social networks"
    - "hedonic games with graph restricted communication"
    - "coalition games on interaction graphs a horticultural perspective"
    - "the authorship dilemma alphabetical or contribution"
    - "on the complexity of core kernel and bargaining set"
    - "cooperative games with overlapping coalitions"
    - "the cost of stability in coalitional games"

Connected through hyperedge HE-5 (induced by source paper "unanimously acceptable agreements for negotiation teams in unpredictable domains"):
  with members:
    - "evolutionary aided negotiation model for bilateral bargaining in ambient intelligence domains with complex utility functions"
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"

Connected through hyperedge HE-6 (induced by source paper "coalition formation and combinatorial auctions applications to self organization and self management in utility computing"):
  with members:
    - "distributed hierarchical control versus an economic model for cloud resource management"
    - "an auction driven self organizing cloud delivery model"

Connected through hyperedge HE-7 (induced by source paper "coalition structure generation on graphs"):
  with members:
    - "a hierarchical dynamic programming algorithm for optimal coalition structure generation"

Connected through hyperedge HE-8 (induced by source paper "tasks for agent based negotiation teams analysis review and challenges"):
  with members:
    - "studying the impact of negotiation environments on negotiation teams performance"
    - "reaching unanimous agreements within agent based negotiation teams with linear and monotonic utility functions"
    - "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)