human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"

Connected through hyperedge HE-2 (induced by source paper "massively parallel algorithms for distance approximation and spanners"):
  with members:
    - "dynamic algorithms for the massively parallel computation model"
    - "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "faster parallel algorithm for approximate shortest path"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "congested clique algorithms for graph spanners"

Connected through hyperedge HE-3 (induced by source paper "new lower bounds for massively parallel computation from query complexity"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "exponentially faster massively parallel maximal matching"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "greedy and local ratio algorithms in the mapreduce model"

Connected through hyperedge HE-4 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space"):
  with members:
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "deterministic distributed dominating set approximation in the congest model"
    - "efficient deterministic distributed coloring with small bandwidth"
    - "exponentially faster massively parallel maximal matching"
    - "congested clique algorithms for graph spanners"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"

Question: Please tell me which class the center node 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)