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: "distributed and efficient resource balancing among many suppliers and consumers"
  Abstract: Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains---smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed supplier and consumer agents, with no inter-agent communication. We simulate a scenario of a market with $S$ suppliers and $C$ consumers such that at every instant, each supplier agent supplies a certain quantity and simultaneously, each consumer agent consumes a certain quantity. The information about the total amount supplied and consumed is only kept with the center. The proposed algorithm is a combination of the classical additive-increase multiplicative-decrease (AIMD) algorithm in conjunction with a probabilistic rule for the agents to respond to a capacity signal. This leads to a nonhomogeneous Markov chain and we show almost sure convergence of this chain to the social optimum, for our market of distributed supplier and consumer agents. Employing this AIMD-type algorithm, the center sends a feedback message to the agents in the supplier side if there is a scenario of excess supply, or to the consumer agents if there is excess consumption. Each agent has a concave utility function whose derivative tends to 0 when an optimum quantity is supplied/consumed. Hence when social convergence is reached, each agent supplies or consumes a quantity which leads to its individual maximum profit, without the need of any communication. So eventually, each agent supplies or consumes a quantity which leads to its individual maximum profit, without communicating with any other agents. Our simulations show the efficacy of this approach.

Connected through hyperedge HE-1 (induced by source paper "on the control of agents coupled through shared unit demand resources"):
  with members:
    - "distributed algorithms for internet of things enabled prosumer markets a control theoretic perspective"
    - "derandomized distributed multi resource allocation with little communication overhead"
    - "distributed private and derandomized allocation algorithm for ev charging"
    - "smart charging for electric vehicles a survey from the algorithmic perspective"

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)