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 "graph sparsification for derandomizing massively parallel computation with low space"):
  Abstract: The Massively Parallel Computation (MPC) model is an emerging model which distills core aspects of distributed and parallel computation. The aim is to solve (typically graph) problems in systems where the input is distributed over many machines with limited space. Recent work has focused on the regime in which machines have sublinear memory, with randomized algorithms presented for the tasks of Maximal Matching, Maximal Independent Set (MIS) and $(\Delta+1)$-Coloring. However, there have been no prior corresponding deterministic algorithms. #R##N#We introduce a new \emph{graph sparsification technique} that \emph{deterministically} computes a low-degree subgraph with additional desired properties. The degree of the nodes in this subgraph is small in the sense that the edges of each node can be now stored on a single machine. Depending on the specific local problem, the low-degree subgraph also has the property that solving the problem on this subgraph provides a \emph{large} global progress. We obtain the following \emph{deterministic} MPC algorithms with $O(n^{\epsilon})$ space on each machine for any constant $\epsilon <1$: #R##N#- We show an $O(\log \Delta+\log\log n)$ communication round algorithms for solving maximal matching and MIS, by derandomizing the well-known randomized algorithm of Luby [SICOMP'86]. Based on the recent work of Ghaffari et al. [FOCS'18], this additive $O(\log\log n)$ factor is \emph{conditionally} essential. These algorithms can also be shown to run in $O(\log \Delta)$ rounds in the closely related model of \congc. This improves up on the state-of-the-art bound of $O(\log^2 \Delta)$ rounds by Censor-Hillel et al. [DISC'17]. #R##N#- By employing our graph sparsification technique accompanied with a palette sparsification, we give a deterministic (deg+1)-list coloring (and thus also a $(\Delta+1)$-coloring) algorithm in $O(\log \Delta+\log\log n)$ rounds.

Includes members:
  Member "walking randomly massively and efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "parallel approximate undirected shortest paths via low hop emulators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "deterministic distributed dominating set approximation in the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient deterministic distributed coloring with small bandwidth")
  Member "efficient deterministic distributed coloring with small bandwidth":
    also connected through other hyperedges:
      (none)
  Member "exponentially faster massively parallel maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "congested clique algorithms for graph spanners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "massively parallel algorithms for finding well connected components in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)