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: "tight bounds for linear sketches of approximate matchings"
  Abstract: We resolve the space complexity of linear sketches for approximating the maximum matching problem in dynamic graph streams where the stream may include both edge insertion and deletion. Specifically, we show that for any $\epsilon > 0$, there exists a one-pass streaming algorithm, which only maintains a linear sketch of size $\tilde{O}(n^{2-3\epsilon})$ bits and recovers an $n^\epsilon$-approximate maximum matching in dynamic graph streams, where $n$ is the number of vertices in the graph. In contrast to the extensively studied insertion-only model, to the best of our knowledge, no non-trivial single-pass streaming algorithms were previously known for approximating the maximum matching problem on general dynamic graph streams. #R##N#Furthermore, we show that our upper bound is essentially tight. Namely, any linear sketch for approximating the maximum matching to within a factor of $O(n^\epsilon)$ has to be of size $n^{2-3\epsilon -o(1)}$ bits. We establish this lower bound by analyzing the corresponding simultaneous number-in-hand communication model, with a combinatorial construction based on Ruzsa-Szemer\'{e}di graphs.

Connected through hyperedge HE-1 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "on the multiparty communication complexity of testing triangle freeness"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "on approximating functions of the singular values in a stream"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-2 (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"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "streaming verification of graph properties"):
  with members:
    - "bipartite perfect matching is in quasi nc"
    - "rational proofs with multiple provers"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "sketching cuts in graphs and hypergraphs"
    - "an improved interactive streaming algorithm for the distinct elements problem"
    - "semi streaming algorithms for annotated graph streams"
    - "streaming lower bounds for approximating max cut"

Connected through hyperedge HE-4 (induced by source paper "almost optimal streaming algorithms for coverage problems"):
  with members:
    - "submodular optimization under noise"
    - "distributed coverage maximization via sketching"
    - "better streaming algorithms for the maximum coverage problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "randomized composable core sets for distributed submodular maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-5 (induced by source paper "kernelization via sampling with applications to dynamic graph streams"):
  with members:
    - "correlation clustering in data streams"
    - "densest subgraph in dynamic graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "sketching cuts in graphs and hypergraphs"
    - "semi streaming set cover"

Connected through hyperedge HE-6 (induced by source paper "densest subgraph in dynamic graph streams"):
  with members:
    - "correlation clustering in data streams"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "single pass spectral sparsification in dynamic streams"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-7 (induced by source paper "sublinear estimation of weighted matchings in dynamic data streams"):
  with members:
    - "kernelization via sampling with applications to dynamic graph streams"
    - "maximum matching in turnstile streams"
    - "better bounds for matchings in the streaming model"
    - "improved bounds for online preemptive matching"
    - "maximum matching in semi streaming with few passes"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "graph sparsification in the semi streaming model"
    - "weighted matching in the semi streaming model"

Connected through hyperedge HE-8 (induced by source paper "maximum matching in turnstile streams"):
  with members:
    - "single pass spectral sparsification in dynamic streams"
    - "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"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "weighted matching in the semi streaming model"

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)