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 "walking randomly massively and efficiently"):
  Abstract: We introduce an approach that enables for efficiently generating many independent random walks in big graph models, such as the Massive Parallel Computation (MPC) model. We consider the case where the space per machine is strongly sublinear in the number of vertices. In this case, many natural approaches for graph problems struggle to overcome the $\Theta(\log n)$ MPC round complexity barrier. We design a PageRank algorithm that break this barrier even for directed graphs, and also show how to break this barrier for bipartiteness and expansion testing. #R##N#In the undirected case we start our random walks from the stationary distribution, so we approximately know the empirical distribution of their next steps. This way we can use doubling approach to prepare continuations of sampled walks in advance. Our approach enables generating multiple random walks of length $l$ in $\Theta(\log l)$ rounds on MPC. Moreover, we show that under \textsc{2-Cycle} conjecture this round complexity is asymptotically tight. One of the most important application of random walks is PageRank computation. We show how to use our approach to compute approximate PageRank w.h.p. for constant damping factor in $O(\log \log n)$ rounds on undirected graphs (with $\tilde{O}(m)$ total space), and $\tilde{O}(\log \log n)$ rounds on directed graphs (with $\tilde{O}(m+n^{1+o(1)})$ total space). #R##N#Building on our random-walk primitive and traditional property testing algorithms, we also show how to approximately test bipartiteness and expansion in $O(\log\log(n))$ MPC rounds.

Includes members:
  Member "exponentially faster massively parallel maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "simulating random walks on graphs in the streaming model":
    also connected through other hyperedges:
      (none)
  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 "testing graph clusterability algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph clustering via qubo and digital annealing")
  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 "weighted matchings via unweighted augmentations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "round compression for parallel graph algorithms in strongly sublinear space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")

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)