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 "adapting sequential algorithms to the distributed setting"):
  Abstract: It is a well known fact that sequential algorithms which exhibit a strong "local" nature can be adapted to the distributed setting given a legal graph coloring. The running time of the distributed algorithm will then be at least the number of colors. Surprisingly, this well known idea was never formally stated as a unified framework. In this paper we aim to define a robust family of local sequential algorithms which can be easily adapted to the distributed setting. We then develop new tools to further enhance these algorithms, achieving state of the art results for fundamental problems. #R##N#We define a simple class of greedy-like algorithms which we call \emph{orderless-local} algorithms. We show that given a legal $c$-coloring of the graph, every algorithm in this family can be converted into a distributed algorithm running in $O(c)$ communication rounds in the CONGEST model. We show that this family is indeed robust as both the method of conditional expectations and the unconstrained submodular maximization algorithm of Buchbinder \etal \cite{BuchbinderFNS15} can be expressed as orderless-local algorithms for \emph{local utility functions} --- Utility functions which have a strong local nature to them. #R##N#We use the above algorithms as a base for new distributed approximation algorithms for the weighted variants of some fundamental problems: Max $k$-Cut, Max-DiCut, Max 2-SAT and correlation clustering. We develop algorithms which have the same approximation guarantees as their sequential counterparts, up to a constant additive $\epsilon$ factor, while achieving an $O(\log^* n)$ running time for deterministic algorithms and $O(\epsilon^{-1})$ running time for randomized ones. This improves exponentially upon the currently best known algorithms.

Includes members:
  Member "correlation clustering in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair correlation clustering")
  Member "fast distributed approximation for max cut":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hardness of distributed optimization")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed approximation of maximum independent set and maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "optimal dynamic distributed mis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved dynamic graph coloring")
  Member "large cuts with local algorithms on triangle free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed detection of cliques in dynamic networks")
  Member "combinatorial approximation algorithms for maxcut using random walks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast distributed approximation for max cut")

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)