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: "when distributed computation is communication expensive"
  Abstract: We consider a number of fundamental statistical and graph problems in the message-passing model, where we have $k$ machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the $k$ data sets. The communication is point-to-point, and the goal is to minimize the total communication among the $k$ machines. This model captures all point-to-point distributed computational models with respect to minimizing communication costs. Our analysis shows that exact computation of many statistical and graph problems in this distributed setting requires a prohibitively large amount of communication, and often one cannot improve upon the communication of the simple protocol in which all machines send their data to a centralized server. Thus, in order to obtain protocols that are communication-efficient, one has to allow approximation, or investigate the distribution or layout of the data sets.

Connected through hyperedge HE-1 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-2 (induced by source paper "weighted reservoir sampling from distributed streams"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted sampling without replacement from data streams"
    - "improved algorithms for distributed entropy monitoring"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "streaming algorithms from precision sampling"
    - "optimal tracking of distributed heavy hitters and quantiles"

Connected through hyperedge HE-3 (induced by source paper "graph spanners in the message passing model"):
  with members:
    - "the 4 3 additive spanner exponent is tight"
    - "distributed computation of large scale graph problems"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "tight bounds for distributed functional monitoring"

Connected through hyperedge HE-4 (induced by source paper "tight bounds on the round complexity of the distributed maximum coverage problem"):
  with members:
    - "distributed partial clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "on estimating maximum matching size in graph streams"
    - "combinatorial auctions do need modest interaction"
    - "submodular optimization over sliding windows"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "better streaming algorithms for the maximum coverage problem"

Connected through hyperedge HE-5 (induced by source paper "batch sparse recovery or how to leverage the average sparsity"):
  with members:
    - "lower bounds for adaptive sparse recovery"
    - "on the power of adaptivity in sparse recovery"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-6 (induced by source paper "on the multiparty communication complexity of testing triangle freeness"):
  with members:
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "distributed testing of excluded subgraphs"
    - "a hybrid sampling scheme for triangle counting"
    - "fast distributed algorithms for testing graph properties"
    - "dynamic graph stream algorithms in o n space"
    - "approximately counting triangles in sublinear time"
    - "algebraic methods in the congested clique"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-7 (induced by source paper "communication complexity of approximate maximum matching in the message passing model"):
  with members:
    - "welfare maximization with limited interaction"
    - "maximum matching in turnstile streams"
    - "economic efficiency requires interaction"
    - "better bounds for matchings in the streaming model"
    - "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
    - "maximum matching in semi streaming with few passes"
    - "sorting searching and simulation in the mapreduce framework"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-8 (induced by source paper "tight bounds for distributed graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "near optimal clustering in the k machine model"
    - "worst case optimal algorithms for parallel query processing"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "the input output complexity of triangle enumeration"

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)