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 "exponentially faster massively parallel maximal matching"):
  Abstract: The study of graph problems in the Massively Parallel Computations (MPC) model has recently seen a burst of breakthroughs. Czumaj et al. [STOC'18], Assadi et al. [SODA'19], and Ghaffari et al. [PODC'18], gave algorithms for finding a $1+\varepsilon$ approximate maximum matching in $O(\log \log n)$ rounds using $\widetilde{O}(n)$ memory per machine. Despite this progress, we still have a far more limited understanding of the central symmetry-breaking problem of maximal matching. The round complexity of all these algorithms blows up to $\Omega(\log n)$ in this case, which is considered inefficient. In fact, the only known subpolylogarithmic round algorithm remains to be that of Lattanzi et al. [SPAA'11] which undesirably requires a strictly super-linear space of $n^{1+\Omega(1)}$ per machine. #R##N#In this work, we close this gap by providing a novel analysis of an extremely simple and well-known algorithm. The algorithm edge-samples the graph, partitions the vertices at random, and finds a greedy maximal matching within each partition. We show that this algorithm drastically reduces the vertex degrees. This, among some other results, leads to an $O(\log \log \Delta)$ round algorithm for maximal matching with $O(n)$ space. The space can be further improved to mildly sublinear in $n$ by standard techniques. #R##N#As an immediate corollary, we get a $2$ approximation for minimum vertex cover in essentially the same rounds and space. This is the best possible approximation factor under standard assumptions, culminating a long line of research. Other corollaries include more efficient algorithms for $1 + \varepsilon$ approximate matching and $2 + \varepsilon$ approximate weighted matching. All these results can also be implemented in the congested clique model within $O(\log \log \Delta)$ rounds.

Includes members:
  Member "massively parallel algorithms for finding well connected components in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "weighted matchings via unweighted augmentations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity")
  Member "correlation clustering in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fair correlation clustering")
  Member "round compression for parallel graph algorithms in strongly sublinear space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "simple round compression for parallel vertex cover":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

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)