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 subgraph detection"):
  Abstract: In the standard CONGEST model for distributed network computing, it is known that "global" tasks such as minimum spanning tree, diameter, and all-pairs shortest paths, consume large bandwidth, for their running-time is $\Omega(\mbox{poly}(n))$ rounds in $n$-node networks with constant diameter. Surprisingly, "local" tasks such as detecting the presence of a 4-cycle as a subgraph also requires $\widetilde{\Omega}(\sqrt{n})$ rounds, even using randomized algorithms, and the best known upper bound for detecting the presence of a 3-cycle is $\widetilde{O}(n^{\frac{2}{3}})$ rounds. The objective of this paper is to better understand the landscape of such subgraph detection tasks. We show that, in contrast to \emph{cycles}, which are hard to detect in the CONGEST model, there exists a deterministic algorithm for detecting the presence of a subgraph isomorphic to $T$ running in a \emph{constant} number of rounds, for every tree $T$. Our algorithm provides a distributed implementation of a combinatorial technique due to Erd\H{o}s et al. for sparsening the set of partial solutions kept by the nodes at each round. Our result has important consequences to \emph{distributed property-testing}, i.e., to randomized algorithms whose aim is to distinguish between graphs satisfying a property, and graphs far from satisfying that property. In particular, we get that, for every graph pattern $H$ composed of an edge and a tree connected in an arbitrary manner, there exists a distributed testing algorithm for $H$-freeness, performing in a constant number of rounds. Although the class of graph patterns $H$ formed by a tree and an edge connected arbitrarily may look artificial, all previous results of the literature concerning testing $H$-freeness for classical patterns such as cycles and cliques can be viewed as direct consequences of our result, while our algorithm enables testing more complex patterns.

Includes members:
  Member "near linear lower bounds for distributed distance computations even in sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "fast distributed algorithms for testing graph properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "algebraic methods in the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "fast partial distance estimation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast approximate shortest paths in the congested clique")
  Member "distributed approximation algorithms for weighted shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "tri tri again finding triangles and small subgraphs in a distributed setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "subgraph isomorphism in planar graphs and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets")

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)