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 testing of graph isomorphism in the congest model"):
  Abstract: In this paper we study the problem of testing graph isomorphism (GI) in the CONGEST distributed model. In this setting we test whether the distributive network, $G_U$, is isomorphic to $G_K$ which is given as an input to all the nodes in the network, or alternatively, only to a single node. #R##N#We first consider the decision variant of the problem in which the algorithm distinguishes $G_U$ and $G_K$ which are isomorphic from $G_U$ and $G_K$ which are not isomorphic. We provide a randomized algorithm with $O(n)$ rounds for the setting in which $G_K$ is given only to a single node. We prove that for this setting the number of rounds of any deterministic algorithm is $\tilde{\Omega}(n^2)$ rounds, where $n$ denotes the number of nodes, which implies a separation between the randomized and the deterministic complexities of deciding GI. #R##N#We then consider the \emph{property testing} variant of the problem, where the algorithm is only required to distinguish the case that $G_U$ and $G_K$ are isomorphic from the case that $G_U$ and $G_K$ are \emph{far} from being isomorphic (according to some predetermined distance measure). We show that every algorithm requires $\Omega(D)$ rounds, where $D$ denotes the diameter of the network. This lower bound holds even if all the nodes are given $G_K$ as an input, and even if the message size is unbounded. We provide a randomized algorithm with an almost matching round complexity of $O(D+(\epsilon^{-1}\log n)^2)$ rounds that is suitable for dense graphs. #R##N#We also show that with the same number of rounds it is possible that each node outputs its mapping according to a bijection which is an \emph{approximated} isomorphism. #R##N#We conclude with simple simulation arguments that allow us to obtain essentially tight algorithms with round complexity $\tilde{O}(D)$ for special families of sparse graphs.

Includes members:
  Member "distributed detection of cliques in dynamic networks":
    also connected through other hyperedges:
      (none)
  Member "property testing of planarity in the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low congestion cycle covers and their applications")
  Member "distributed triangle detection via expander decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "detecting cliques in congest networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "distributed subgraph detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed detection of cliques in dynamic networks")
  Member "fooling views a new lower bound technique for distributed computations under congestion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "distributed detection of cycles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "property testing of planarity in the congest model")
  Member "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed detection of cliques in dynamic 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.DS(Data Structures and Algorithms)