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: "graph sparsification by effective resistances"
  Abstract: We present a nearly-linear time algorithm that produces high-quality sparsifiers of weighted graphs. Given as input a weighted graph $G=(V,E,w)$ and a parameter $\epsilon>0$, we produce a weighted subgraph $H=(V,\tilde{E},\tilde{w})$ of $G$ such that $|\tilde{E}|=O(n\log n/\epsilon^2)$ and for all vectors $x\in\R^V$ $(1-\epsilon)\sum_{uv\in E}(x(u)-x(v))^2w_{uv}\le \sum_{uv\in\tilde{E}}(x(u)-x(v))^2\tilde{w}_{uv} \le (1+\epsilon)\sum_{uv\in E}(x(u)-x(v))^2w_{uv}. (*)$ #R##N#This improves upon the sparsifiers constructed by Spielman and Teng, which had $O(n\log^c n)$ edges for some large constant $c$, and upon those of Bencz\'ur and Karger, which only satisfied (*) for $x\in\{0,1\}^V$. #R##N#A key ingredient in our algorithm is a subroutine of independent interest: a nearly-linear time algorithm that builds a data structure from which we can query the approximate effective resistance between any two vertices in a graph in $O(\log n)$ time.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"

Connected through hyperedge HE-3 (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"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "weighted gradient coding with leverage score sampling"):
  with members:
    - "numerically stable binary gradient coding"
    - "fundamental limits of approximate gradient coding"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "balanced reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "randomized spectral clustering in large scale stochastic block models"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "a tutorial on spectral clustering"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "efficient and simple algorithms for fault tolerant spanners"):
  with members:
    - "a trivial yet optimal solution to vertex fault tolerant spanners"
    - "optimal vertex fault tolerant spanners for fixed stretch"
    - "parallel graph decompositions using random shifts"
    - "improved parallel algorithms for spanners and hopsets"
    - "fault tolerant spanners better and simpler"
    - "transitive closure spanners"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

Connected through hyperedge HE-7 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"):
  with members:
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "distributed edge connectivity in sublinear time"
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "flows in almost linear time via adaptive preconditioning"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"

Connected through hyperedge HE-8 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

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)