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 "distributed edge connectivity in sublinear time"):
  Abstract: We present the first sublinear-time algorithm that can compute the edge connectivity λ of a network exactly on distributed message-passing networks (the CONGEST model), as long as the network contains no multi-edge. We present the first sublinear-time algorithm for a distributed message-passing network sto compute its edge connectivity λ exactly in the CONGEST model, as long as there are no parallel edges. Our algorithm takes O(n1−1/353D1/353+n1−1/706) time to compute λ and a cut of cardinality λ with high probability, where n and D are the number of nodes and the diameter of the network, respectively, and O hides polylogarithmic factors. This running time is sublinear in n (i.e. O(n1−є)) whenever D is. Previous sublinear-time distributed algorithms can solve this problem either (i) exactly only when λ=O(n1/8−є) [Thurimella PODC’95; Pritchard, Thurimella, ACM Trans. Algorithms’11; Nanongkai, Su, DISC’14] or (ii) approximately [Ghaffari, Kuhn, DISC’13; Nanongkai, Su, DISC’14]. To achieve this we develop and combine several new techniques. First, we design the first distributed algorithm that can compute a k-edge connectivity certificate for any k=O(n1−є) in time O(√nk+D). The previous sublinear-time algorithm can do so only when k=o(√n) [Thurimella PODC’95]. In fact, our algorithm can be turned into the first parallel algorithm with polylogarithmic depth and near-linear work. Previous near-linear work algorithms are essentially sequential and previous polylogarithmic-depth algorithms require Ω(mk) work in the worst case (e.g. [Karger, Motwani, STOC’93]). Second, we show that by combining the recent distributed expander decomposition technique of [Chang, Pettie, Zhang, SODA’19] with techniques from the sequential deterministic edge connectivity algorithm of [Kawarabayashi, Thorup, STOC’15], we can decompose the network into a sublinear number of clusters with small average diameter and without any mincut separating a cluster (except the “trivial” ones). This leads to a simplification of the Kawarabayashi-Thorup framework (except that we are randomized while they are deterministic). This might make this framework more useful in other models of computation. Finally, by extending the tree packing technique from [Karger STOC’96], we can find the minimum cut in time proportional to the number of components. As a byproduct of this technique, we obtain an O(n)-time algorithm for computing exact minimum cut for weighted graphs.

Includes members:
  Member "improved distributed expander decomposition and nearly optimal triangle enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "small cuts and connectivity certificates a fault tolerant approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hardness of distributed optimization")
  Member "distributed triangle detection via expander decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "distributed exact weighted all pairs shortest paths in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simple and fast distributed computation of betweenness centrality")
  Member "expander decomposition and pruning faster stronger and simpler":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "a faster distributed single source shortest paths algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast approximate shortest paths in the congested clique")
  Member "distributed exact shortest paths in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast approximate shortest paths in the congested clique")

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)