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: "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
  Abstract: Estimating the size of the maximum matching is a canonical problem in graph algorithms, and one that has attracted extensive study over a range of different computational models. We present improved streaming algorithms for approximating the size of maximum matching with sparse (bounded arboricity) graphs. #R##N#* Insert-Only Streams: We present a one-pass algorithm that takes O(c log^2 n) space and approximates the size of the maximum matching in graphs with arboricity c within a factor of O(c). This improves significantly on the state-of-the-art O~(cn^{2/3})-space streaming algorithms. #R##N#* Dynamic Streams: Given a dynamic graph stream (i.e., inserts and deletes) of edges of an underlying c-bounded arboricity graph, we present a one-pass algorithm that uses space O~(c^{10/3}n^{2/3}) and returns an O(c)-estimator for the size of the maximum matching. This algorithm improves the state-of-the-art O~(cn^{4/5})-space algorithms, where the O~(.) notation hides logarithmic in $n$ dependencies. #R##N#* Random Order Streams: For randomly ordered graph streams, we present a two-pass streaming algorithm that outputs a (1+eps)-approximation of the maximal matching size using O(d^{d^{d+6}}) space for graphs with maximum degree bounded by d. The previous best algorithm for this setting is an O(\log d) approximation. Our algorithm is the first with an approximation guarantee independent of d. Our result is obtained by simulating a property-testing result in a streaming setting, a technique which might be of independent interest. #R##N#In contrast to the previous works, our results take more advantage of the streaming access to the input and characterize the matching size based on the ordering of the edges in the stream in addition to the degree distributions and structural properties of the sparse graphs.

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 "towards a theory of parameterized streaming algorithms"):
  with members:
    - "towards tight er bounds for the excluded grid theorem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "polynomial bounds for the grid minor theorem"
    - "directed subset feedback vertex set is fixed parameter tractable"

Connected through hyperedge HE-3 (induced by source paper "almost smooth histograms and sliding window graph algorithms"):
  with members:
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"

Connected through hyperedge HE-4 (induced by source paper "massively parallel symmetry breaking on sparse graphs mis and maximal matching"):
  with members:
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "mst in o 1 rounds of the congested clique"
    - "improved deterministic distributed matching via rounding"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "on estimating maximum matching size in graph streams"
    - "estimating weighted matchings in o n space"

Connected through hyperedge HE-5 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "maximum matching in turnstile streams"
    - "stream sampling for frequency cap statistics"
    - "structure aware sampling flexible and accurate summarization"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "estimating graph parameters from random order streams"):
  with members:
    - "testable bounded degree graph properties are random order streamable"
    - "dynamic graph stream algorithms in o n space"
    - "a note on logarithmic space stream algorithms for matchings in low arboricity graphs"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"

Connected through hyperedge HE-7 (induced by source paper "independent set size approximation in graph streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "space constrained interval selection"

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)