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 "massively parallel algorithms for finding well connected components in sparse graphs"):
  Abstract: A fundamental question that shrouds the emergence of massively parallel computing (MPC) platforms is how can the additional power of the MPC paradigm be leveraged to achieve faster algorithms compared to classical parallel models such as PRAM? #R##N#Previous research has identified the sparse graph connectivity problem as a major obstacle to such improvement: While classical logarithmic-round PRAM algorithms for finding connected components in any $n$-vertex graph have been known for more than three decades, no $o(\log{n})$-round MPC algorithms are known for this task with truly sublinear in $n$ memory per machine. This problem arises when processing massive yet sparse graphs with $O(n)$ edges, for which the interesting setting of parameters is $n^{1-\Omega(1)}$ memory per machine. It is conjectured that achieving an $o(\log{n})$-round algorithm for connectivity on general sparse graphs with $n^{1-\Omega(1)}$ per-machine memory may not be possible, and this conjecture also forms the basis for multiple conditional hardness results on the round complexity of other problems in the MPC model. #R##N#We take an opportunistic approach towards the sparse graph connectivity problem, by designing an algorithm with improved performance guarantees in terms of the connectivity structure of the input graph. Formally, we design an algorithm that finds all connected components with spectral gap at least $\lambda$ in a graph in $O(\log\log{n} + \log{(1/\lambda)})$ MPC rounds and $n^{\Omega(1)}$ memory per machine. As such, this algorithm achieves an exponential round reduction on sparse "well-connected" components (i.e., $\lambda \geq 1/\text{polylog}{(n)}$) using only $n^{\Omega(1)}$ memory per machine and $\widetilde{O}(n)$ total memory, and still operates in $o(\log n)$ rounds even when $\lambda = 1/n^{o(1)}$.

Includes members:
  Member "matching and mis for uniformly sparse graphs in the low memory mpc model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees")
  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 "brief announcement semi mapreduce meets congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space")
  Member "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "the complexity of delta 1 coloring incongested clique 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 "mis in the congested clique model in o log log δ rounds":
    also connected through other hyperedges:
      (none)
  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 "mst in o 1 rounds of the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing")

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)