human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "plurality consensus via shuffling lessons learned from load balancing"):
  Abstract: We consider \emph{plurality consensus} in a network of $n$ nodes. Initially, each node has one of $k$ opinions. The nodes execute a (randomized) distributed protocol to agree on the plurality opinion (the opinion initially supported by the most nodes). Nodes in such networks are often quite cheap and simple, and hence one seeks protocols that are not only fast but also simple and space efficient. Typically, protocols depend heavily on the employed communication mechanism, which ranges from sequential (only one pair of nodes communicates at any time) to fully parallel (all nodes communicate with all their neighbors at once) communication and everything in-between. #R##N#We propose a framework to design protocols for a multitude of communication mechanisms. We introduce protocols that solve the plurality consensus problem and are with probability 1-o(1) both time and space efficient. Our protocols are based on an interesting relationship between plurality consensus and distributed load balancing. This relationship allows us to design protocols that generalize the state of the art for a large range of problem parameters. In particular, we obtain the same bounds as the recent result of Alistarh et al. (who consider only two opinions on a clique) using a much simpler protocol that generalizes naturally to general graphs and multiple opinions.

Includes members:
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed voting in beep model")
  Member "simple dynamics for plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed voting in beep model")
  Member "tight bounds for randomized load balancing on arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel balanced allocations the heavily loaded case")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)