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 "near optimal massively parallel graph connectivity"):
  Abstract: Identifying the connected components of a graph, apart from being a fundamental problem with countless applications, is a key primitive for many other algorithms. In this paper, we consider this problem in parallel settings. Particularly, we focus on the Massively Parallel Computations (MPC) model, which is the standard theoretical model for modern parallel frameworks such as MapReduce, Hadoop, or Spark. We consider the truly sublinear regime of MPC for graph problems where the space per machine is $n^\delta$ for some desirably small constant $\delta \in (0, 1)$. #R##N#We present an algorithm that for graphs with diameter $D$ in the wide range $[\log^{\epsilon} n, n]$, takes $O(\log D)$ rounds to identify the connected components and takes $O(\log \log n)$ rounds for all other graphs. The algorithm is randomized, succeeds with high probability, does not require prior knowledge of $D$, and uses an optimal total space of $O(m)$. We complement this by showing a conditional lower-bound based on the widely believed TwoCycle conjecture that $\Omega(\log D)$ rounds are indeed necessary in this setting. #R##N#Studying parallel connectivity algorithms received a resurgence of interest after the pioneering work of Andoni et al. [FOCS 2018] who presented an algorithm with $O(\log D \cdot \log \log n)$ round-complexity. Our algorithm improves this result for the whole range of values of $D$ and almost settles the problem due to the conditional lower-bound. #R##N#Additionally, we show that with minimal adjustments, our algorithm can also be implemented in a variant of the (CRCW) PRAM in asymptotically the same number of rounds.

Includes members:
  Member "connected components on a pram in log diameter time":
    also connected through other hyperedges:
      (none)
  Member "graph connectivity in log diameter steps using label propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple concurrent labeling algorithms for connected components")
  Member "matching and mis for uniformly sparse graphs in the low memory mpc model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees")
  Member "massively parallel algorithms for finding well connected components in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "approximating edit distance in truly subquadratic time quantum and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "connected components at scale via local contractions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "massively parallel symmetry breaking on sparse graphs mis and maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity")

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)