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: "sublinear estimation of weighted matchings in dynamic data streams"
  Abstract: This paper presents an algorithm for estimating the weight of a maximum weighted matching by augmenting any estimation routine for the size of an unweighted matching. The algorithm is implementable in any streaming model including dynamic graph streams. We also give the first constant estimation for the maximum matching size in a dynamic graph stream for planar graphs (or any graph with bounded arboricity) using $\tilde{O}(n^{4/5})$ space which also extends to weighted matching. Using previous results by Kapralov, Khanna, and Sudan (2014) we obtain a $\mathrm{polylog}(n)$ approximation for general graphs using $\mathrm{polylog}(n)$ space in random order streams, respectively. In addition, we give a space lower bound of $\Omega(n^{1-\varepsilon})$ for any randomized algorithm estimating the size of a maximum matching up to a $1+O(\varepsilon)$ factor for adversarial streams.

Connected through hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "extractor based time space lower bounds for learning"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"

Connected through hyperedge HE-2 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "approximating the spectrum of a graph"
    - "embeddings of schatten norms with applications to data streams"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-3 (induced by source paper "almost smooth histograms and sliding window graph algorithms"):
  with members:
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"

Connected through hyperedge HE-4 (induced by source paper "metric sublinear algorithms via linear sampling"):
  with members:
    - "sublinear algorithms for maxcut and correlation clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "towards tight bounds for the streaming set cover problem"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-5 (induced by source paper "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "the dictionary testing problem"
    - "sketching as a tool for numerical linear algebra"
    - "relations among some low rank subspace recovery models"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-6 (induced by source paper "an optimal space lower bound for approximating max cut"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "maximum matching in turnstile streams"
    - "sketching cuts in graphs and hypergraphs"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"

Connected through hyperedge HE-7 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "on approximating functions of the singular values in a stream"
    - "maximum matching in turnstile streams"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-8 (induced by source paper "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "on estimating maximum matching size in graph streams"
    - "densest subgraph in dynamic graph streams"
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "an improved randomized data structure for dynamic graph connectivity"
    - "maximum matching in turnstile streams"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"

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)