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 "byzantine gathering in polynomial time"):
  Abstract: We study the task of Byzantine gathering in a network modeled as a graph. Despite the presence of Byzantine agents, all the other (good) agents, starting from possibly different nodes and applying the same deterministic algorithm, have to meet at the same node in finite time and stop moving. An adversary chooses the initial nodes of the agents and assigns a different label to each of them. The agents move in synchronous rounds and communicate with each other only when located at the same node. Within the team, f of the agents are Byzantine. A Byzantine agent acts in an unpredictable way: in particular it may forge the label of another agent or create a completely new one. Besides its label, which corresponds to a local knowledge, an agent is assigned some global knowledge GK that is common to all agents. In literature, the Byzantine gathering problem has been analyzed in arbitrary n-node graphs by considering the scenario when GK=(n,f) and the scenario when GK=f. In the first (resp. second) scenario, it has been shown that the minimum number of good agents guaranteeing deterministic gathering of all of them is f+1 (resp. f+2). For both these scenarios, all the existing deterministic algorithms, whether or not they are optimal in terms of required number of good agents, have a time complexity that is exponential in n and L, where L is the largest label belonging to a good agent. #R##N#In this paper, we seek to design a deterministic solution for Byzantine gathering that makes a concession on the proportion of Byzantine agents within the team, but that offers a significantly lower complexity. We also seek to use a global knowledge whose the length of the binary representation is small. Assuming that the agents are in a strong team i.e., a team in which the number of good agents is at least some prescribed value that is quadratic in f, we give positive and negative results.

Includes members:
  Member "search on a line by byzantine robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy consumption of group search on a line")
  Member "byzantine gathering in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "want to gather no need to chatter")
  Member "time versus cost tradeoffs for deterministic rendezvous in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic meeting of sniffing agents in the plane")
  Member "how to meet asynchronously at polynomial cost":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "want to gather no need to chatter")
  Member "how to meet asynchronously almost everywhere":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "positional encoding by robots with non rigid movements")

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)