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: "hedonic coalition formation for distributed task allocation among wireless agents"
  Abstract: Autonomous wireless agents such as unmanned aerial vehicles, mobile base stations, cognitive devices, or self-operating wireless nodes present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software engineering applications, this paper proposes a novel usage model for self-organizing agents suitable for wireless communication networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents, designated as collectors, which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents might also take the role of a relay for improving the packet success rate of the transmission. The proposed hedonic coalition formation algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of polling system delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed coalition formation algorithm allows the agents and tasks to adapt the topology to environmental changes, such as the arrival of new tasks, the removal of existing tasks, or the mobility of the tasks. Simulation results show how the proposed algorithm allows the agents and tasks to self-organize into independent coalitions, while improving the performance, in terms of average player (agent or task) payoff, of at least 30.26 percent (for a network of five agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.

Connected through hyperedge HE-1 (induced by source paper "computational complexity of hedonic games on sparse graphs"):
  with members:
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "stable partitions in additively separable hedonic games"
    - "settling the complexity of local max cut almost completely"

Connected through hyperedge HE-2 (induced by source paper "anonymous hedonic game for task allocation in a large scale multiple agent system"):
  with members:
    - "group activity selection problem"
    - "existence of stability in hedonic coalition formation games"

Connected through hyperedge HE-3 (induced by source paper "overlapping coalition formation games for emerging communication networks"):
  with members:
    - "coalitional graph games for popular content distribution in cognitive radio vanets"
    - "what will 5g be"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"
    - "cooperative games with overlapping coalitions"
    - "coalition formation games for distributed cooperation among roadside units in vehicular networks"
    - "physical layer security coalitional games for distributed cooperation"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "a distributed merge and split algorithm for fair cooperation in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "general auction theoretic strategies for distributed partner selection in cooperative wireless networks"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-5 (induced by source paper "maximizing profit in green cellular networks through collaborative games"):
  with members:
    - "the coalitional switch off game of service providers"
    - "base station selection for energy efficient network operation with the majorization minimization algorithm"
    - "modeling energy consumption in cellular networks"
    - "on the role of infrastructure sharing for mobile network operators in emerging markets"

Connected through hyperedge HE-6 (induced by source paper "a game theoretic approach to distributed coalition formation in energy aware cloud federations extended version"):
  with members:
    - "on the nash stability in the hedonic coalition formation games"
    - "existence of stability in hedonic coalition formation games"
    - "coalition formation games for distributed cooperation among roadside units in vehicular networks"
    - "a generic approach to coalition formation"

Connected through hyperedge HE-7 (induced by source paper "the coalitional switch off game of service providers"):
  with members:
    - "a tractable approach to coverage and rate in cellular networks"
    - "coalitional game theory for communication networks a tutorial"
    - "a generic approach to coalition formation"

Connected through hyperedge HE-8 (induced by source paper "on the payoff mechanisms in peer assisted services with multiple content providers rationality and fairness"):
  with members:
    - "on the shapley like payoff mechanisms in peer assisted services with multiple content providers"

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.IT(Information Theory)