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: "leader election for anonymous asynchronous agents in arbitrary networks"
  Abstract: We consider the problem of leader election among mobile agents operating in an arbitrary network modeled as an undirected graph. Nodes of the network are unlabeled and all agents are identical. Hence the only way to elect a leader among agents is by exploiting asymmetries in their initial positions in the graph. Agents do not know the graph or their positions in it, hence they must gain this knowledge by navigating in the graph and share it with other agents to accomplish leader election. This can be done using meetings of agents, which is difficult because of their asynchronous nature: an adversary has total control over the speed of agents. When can a leader be elected in this adversarial scenario and how to do it? We give a complete answer to this question by characterizing all initial configurations for which leader election is possible and by constructing an algorithm that accomplishes leader election for all configurations for which this can be done.

Connected through hyperedge HE-1 (induced by source paper "almost logarithmic time space optimal leader election in population protocols"):
  with members:
    - "fast space optimal leader election in population protocols"
    - "logarithmic expected time leader election in population protocol model"
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "on convergence and threshold properties of discrete lotka volterra population protocols"
    - "stable leader election in population protocols requires linear time"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-2 (induced by source paper "impact of knowledge on election time in anonymous networks"):
  with members:
    - "deterministic leader election in o d log n time with messages of size o 1"
    - "topology recognition with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "knowledge level of symmetry and time of leader election"
    - "election vs selection two ways of finding the largest node in a graph"

Connected through hyperedge HE-3 (induced by source paper "topology recognition and leader election in colored networks"):
  with members:
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "knowledge level of symmetry and time of leader election"
    - "election vs selection two ways of finding the largest node in a graph"

Connected through hyperedge HE-4 (induced by source paper "anonymous processors with synchronous shared memory"):
  with members:
    - "naming a channel with beeps"
    - "asynchronous exclusive selection"
    - "naming and counting in anonymous unknown dynamic networks"
    - "anonymous meeting in networks"
    - "on the importance of having an identity or is consensus really universal"

Connected through hyperedge HE-5 (induced by source paper "time vs information tradeoffs for leader election in anonymous trees"):
  with members:
    - "knowledge level of symmetry and time of leader election"
    - "election vs selection two ways of finding the largest node in a graph"

Connected through hyperedge HE-6 (induced by source paper "distinguishing views in symmetric networks a tight lower bound"):
  with members:
    - "knowledge level of symmetry and time of leader election"
    - "decidability classes for mobile agents computing"

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.DC(Distributed, Parallel, and Cluster Computing)