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: "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
  Abstract: We introduce a method for sparsifying distributed algorithms and exhibit how it leads to improvements that go past known barriers in two algorithmic settings of large-scale graph processing: Massively Parallel Computation (MPC), and Local Computation Algorithms (LCA). #R##N#- MPC with Strongly Sublinear Memory: Recently, there has been growing interest in obtaining MPC algorithms that are faster than their classic $O(\log n)$-round parallel counterparts for problems such as MIS, Maximal Matching, 2-Approximation of Minimum Vertex Cover, and $(1+\epsilon)$-Approximation of Maximum Matching. Currently, all such MPC algorithms require $\tilde{\Omega}(n)$ memory per machine. Czumaj et al. [STOC'18] were the first to handle $\tilde{\Omega}(n)$ memory, running in $O((\log\log n)^2)$ rounds. We obtain $\tilde{O}(\sqrt{\log \Delta})$-round MPC algorithms for all these four problems that work even when each machine has memory $n^{\alpha}$ for any constant $\alpha\in (0, 1)$. Here, $\Delta$ denotes the maximum degree. These are the first sublogarithmic-time algorithms for these problems that break the linear memory barrier. #R##N#- LCAs with Query Complexity Below the Parnas-Ron Paradigm: Currently, the best known LCA for MIS has query complexity $\Delta^{O(\log \Delta)} poly(\log n)$, by Ghaffari [SODA'16]. As pointed out by Rubinfeld, obtaining a query complexity of $poly(\Delta\log n)$ remains a central open question. Ghaffari's bound almost reaches a $\Delta^{\Omega\left(\frac{\log \Delta}{\log\log \Delta}\right)}$ barrier common to all known MIS LCAs, which simulate distributed algorithms by learning the local topology, a la Parnas-Ron [TCS'07]. This barrier follows from the $\Omega(\frac{\log \Delta}{\log\log \Delta})$ distributed lower bound of Kuhn, et al. [JACM'16]. We break this barrier and obtain an MIS LCA with query complexity $\Delta^{O(\log\log \Delta)} poly(\log n)$.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "parallel algorithms for small subgraph counting"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "linear time subgraph counting graph degeneracy and the chasm at size six"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "weighted matchings via unweighted augmentations"

Connected through hyperedge HE-3 (induced by source paper "massively parallel algorithms for distance approximation and spanners"):
  with members:
    - "walking randomly massively and efficiently"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model"):
  with members:
    - "how fast can you update your mst dynamic algorithms for cluster computing"
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "parallel batch dynamic graph connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "fully dynamic maximal independent set in expected poly log update time"
    - "a deterministic algorithm for the mst problem in constant rounds of congested clique"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"

Connected through hyperedge HE-5 (induced by source paper "new lower bounds for massively parallel computation from query complexity"):
  with members:
    - "walking randomly massively and efficiently"
    - "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"
    - "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-6 (induced by source paper "distributed maximum matching verification in congest"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "improved deterministic distributed matching via rounding"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "on derandomizing local distributed algorithms"
    - "distributed approximation of maximum independent set and maximum matching"

Connected through hyperedge HE-7 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "mst in o 1 rounds of the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-8 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "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"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "weighted matchings via unweighted augmentations"

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)