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 "fast and compact self stabilizing verification computation and fault detection of an mst"):
  Abstract: This paper demonstrates the usefulness of distributed local verification of proofs, as a tool for the design of self-stabilizing algorithms. In particular, it introduces a somewhat generalized notion of distributed local proofs, and utilizes it for improving the time complexity significantly, while maintaining space optimality. As a result, we show that optimizing the memory size carries at most a small cost in terms of time, in the context of minimum spanning tree (MST). That is, we present algorithms that are both time and space efficient for both constructing an MST and for verifying it. This involves several parts that may be considered contributions in themselves. First, we generalize the notion of local proofs, trading off the time complexity for memory efficiency. This adds a dimension to the study of distributed local proofs, which has been gaining attention recently. Specifically, we design a (self-stabilizing) proof labeling scheme which is memory optimal (i.e., $$O(\log n)$$O(logn) bits per node), and whose time complexity is $$O(\log ^2 n)$$O(log2n) in synchronous networks, or $$O(\varDelta \log ^3 n)$$O(Δlog3n) time in asynchronous ones, where $$\varDelta $$Δ is the maximum degree of nodes. This answers an open problem posed by Awerbuch et al. (1991). We also show that $$\varOmega (\log n)$$Ω(logn) time is necessary, even in synchronous networks. Another property is that if $$f$$f faults occurred, then, within the required detection time above, they are detected by some node in the $$O(f\log n)$$O(flogn) locality of each of the faults. Second, we show how to enhance a known transformer that makes input/output algorithms self-stabilizing. It now takes as input an efficient construction algorithm and an efficient self-stabilizing proof labeling scheme, and produces an efficient self-stabilizing algorithm. When used for MST, the transformer produces a memory optimal self-stabilizing algorithm, whose time complexity, namely, $$O(n)$$O(n), is significantly better even than that of previous algorithms (the time complexity of previous MST algorithms that used $$\varOmega (\log ^2 n)$$Ω(log2n) memory bits per node was $$O(n^2)$$O(n2), and the time for optimal space algorithms was $$O(n|E|)$$O(n|E|)). Inherited from our proof labeling scheme, our self-stabilising MST construction algorithm also has the following two properties: (1) if faults occur after the construction ended, then they are detected by some nodes within $$O(\log ^2 n)$$O(log2n) time in synchronous networks, or within $$O(\varDelta \log ^3 n)$$O(Δlog3n) time in asynchronous ones, and (2) if $$f$$f faults occurred, then, within the required detection time above, they are detected within the $$O(f\log n)$$O(flogn) locality of each of the faults. We also show how to improve the above two properties, at the expense of some increase in the memory.

Includes members:
  Member "fast self stabilizing minimum spanning tree construction using compact nearest common ancestor labeling scheme":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on underlay aware self stabilizing overlay networks")
  Member "randomized distributed decision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "introduction to local certification")
  Member "on the impact of identifiers on local decision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proof labeling schemes broadcast unicast and in between")
  Member "distributed verification and hardness of distributed approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "local distributed decision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local distributed algorithms in highly dynamic networks")
  Member "a new self stabilizing minimum spanning tree construction with loop free property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on underlay aware self stabilizing overlay networks")

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.DC(Distributed, Parallel, and Cluster Computing)