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: "dynamic streaming spectral sparsification in nearly linear time and space"
  Abstract: In this paper we consider the problem of computing spectral approximations to graphs in the single pass dynamic streaming model. We provide a linear sketching based solution that given a stream of edge insertions and deletions to a $n$-node undirected graph, uses $\tilde O(n)$ space, processes each update in $\tilde O(1)$ time, and with high probability recovers a spectral sparsifier in $\tilde O(n)$ time. Prior to our work, state of the art results either used near optimal $\tilde O(n)$ space complexity, but brute-force $\Omega(n^2)$ recovery time [Kapralov et al.'14], or with subquadratic runtime, but polynomially suboptimal space complexity [Ahn et al.'14, Kapralov et al.'19]. #R##N#Our main technical contribution is a novel method for `bucketing' vertices of the input graph into clusters that allows fast recovery of edges of sufficiently large effective resistance. Our algorithm first buckets vertices of the graph by performing ball-carving using (an approximation to) its effective resistance metric, and then recovers the high effective resistance edges from a sketched version of an electrical flow between vertices in a bucket, taking nearly linear time in the number of vertices overall. This process is performed at different geometric scales to recover a sample of edges with probabilities proportional to effective resistances and obtain an actual sparsifier of the input graph. #R##N#This work provides both the first efficient $\ell_2$-sparse recovery algorithm for graphs and new primitives for manipulating the effective resistance embedding of a graph, both of which we hope have further applications.

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "optimality of the johnson lindenstrauss lemma"

Connected through hyperedge HE-2 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams"):
  with members:
    - "approximate maximum matching in random streams"
    - "polynomial pass lower bounds for graph streaming algorithms"
    - "weighted matchings via unweighted augmentations"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "simplified and space optimal semi streaming for 2 epsilon approximate matching"
    - "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-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-4 (induced by source paper "faster spectral sparsification in dynamic streams"):
  with members:
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "graph clustering using effective resistance"
    - "edit distance sketching streaming and document exchange"
    - "on fully dynamic graph sparsifiers"

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)