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: "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.

Connected through hyperedge HE-1 (induced by source paper "distributed testing of graph isomorphism in the congest model"):
  with members:
    - "distributed detection of cliques in dynamic networks"
    - "property testing of planarity in the congest model"
    - "distributed triangle detection via expander decomposition"
    - "detecting cliques in congest networks"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "distributed detection of cycles"
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"
    - "triangle finding and listing in congest networks"

Connected through hyperedge HE-2 (induced by source paper "distributed detection of cliques in dynamic networks"):
  with members:
    - "fast and simple deterministic algorithms for highly dynamic networks"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "local distributed algorithms in highly dynamic networks"
    - "distributed triangle detection via expander decomposition"
    - "improved algorithms for fully dynamic maximal independent set"
    - "detecting cliques in congest networks"
    - "fooling views a new lower bound technique for distributed computations under congestion"

Connected through hyperedge HE-3 (induced by source paper "property testing of planarity in the congest model"):
  with members:
    - "minor excluded network families admit fast distributed algorithms"
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "testing bounded arboricity"
    - "a sublinear tester for outerplanarity and other forbidden minors with one sided error"
    - "distributed detection of cycles"
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"
    - "distributed property testing for subgraph freeness revisited"
    - "on the multiparty communication complexity of testing triangle freeness"

Connected through hyperedge HE-4 (induced by source paper "deterministic subgraph detection in broadcast congest"):
  with members:
    - "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"
    - "distributed property testing for subgraph freeness revisited"
    - "tight bounds for distributed graph computations"
    - "distributed testing of excluded subgraphs"
    - "fast distributed algorithms for testing graph properties"
    - "algebraic methods in the congested clique"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"
    - "the locality of distributed symmetry breaking"

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)