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: "computing exact distances in the congested clique"
  Abstract: This paper gives simple distributed algorithms for the fundamental problem of computing graph distances in the Congested Clique model. One of the main components of our algorithms is fast matrix multiplication, for which we show an $O(n^{1/3})$-round algorithm when the multiplication needs to be performed over a semi-ring, and an $O(n^{0.157})$-round algorithm when the computation can be performed over a field. We propose to denote by $\kappa$ the exponent of matrix multiplication in this model, which gives $\kappa < 0.157$. #R##N#We show how to compute all-pairs-shortest-paths (APSP) in $O(n^{1/3}\log{n})$ rounds in weighted graphs of $n$ nodes, implying also the computation of the graph diameter $D$. In unweighted graphs, APSP can be computed in $O(\min\{n^{1/3}\log{D},n^{\kappa} D\})$ rounds, and the diameter can be computed in $O(n^{\kappa}\log{D})$ rounds. Furthermore, we show how to compute the girth of a graph in $O(n^{1/3})$ rounds, and provide triangle detection and 4-cycle detection algorithms that complete in $O(n^{\kappa})$ rounds. #R##N#All our algorithms are deterministic. Our triangle detection and 4-cycle detection algorithms improve upon the previously best known algorithms in this model, and refute a conjecture that $\tilde \Omega (n^{1/3})$ rounds are required for detecting triangles by any deterministic oblivious algorithm. Our distance computation algorithms are exact, and improve upon the previously best known $\tilde O(n^{1/2})$ algorithm of Nanongkai [STOC 2014] for computing a $(2+o(1))$-approximation of APSP. #R##N#Finally, we give lower bounds that match the above for natural families of algorithms. For the Congested Clique Broadcast model, we derive unconditioned lower bounds for matrix multiplication and APSP. The matrix multiplication algorithms and lower bounds are adapted from parallel computations, which is a connection of independent interest.

Connected through hyperedge HE-1 (induced by source paper "fast distributed algorithms for connectivity and mst in large graphs"):
  with members:
    - "near optimal clustering in the k machine model"
    - "tight bounds for distributed graph computations"
    - "on the distributed complexity of large scale graph computations"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "super fast mst algorithms in the congested clique using o m messages"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "algebraic methods in the congested clique"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"

Connected through hyperedge HE-2 (induced by source paper "almost optimal distributed algorithms for large scale graph problems"):
  with members:
    - "algebraic methods in the congested clique"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "distributed approximation algorithms for weighted shortest paths"
    - "algebrisation in distributed graph algorithms fast matrix multiplication in the congested clique"
    - "distributed minimum cut approximation"
    - "distributed computation of large scale graph problems"
    - "when distributed computation is communication expensive"
    - "optimal deterministic routing and sorting on the congested clique"

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)