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: "sparse sums of positive semidefinite matrices"
  Abstract: Many fast graph algorithms begin by preprocessing the graph to improve its sparsity. A common form of this is spectral sparsification, which involves removing and reweighting the edges of the graph while approximately preserving its spectral properties. This task has a more general linear algebraic formulation in terms of approximating sums of rank-one matrices. This article considers a more general task of approximating sums of symmetric, positive semidefinite matrices of arbitrary rank. We present two deterministic, polynomial time algorithms for solving this problem. The first algorithm applies the pessimistic estimators of Wigderson and Xiao, and the second involves an extension of the method of Batson, Spielman, and Srivastava. These algorithms have several applications, including sparsifiers of hypergraphs, sparse solutions to semidefinite programs, sparsifiers of unique games, and graph sparsifiers with various auxiliary constraints.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "finding sparse solutions for packing and covering semidefinite programs"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "a parallel approximation algorithm for mixed packing and covering semidefinite programs"
    - "a parallel approximation algorithm for positive semidefinite programming"
    - "twice ramanujan sparsifiers"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-4 (induced by source paper "on fully dynamic graph sparsifiers"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "algorithms for lipschitz learning on graphs"
    - "sketching cuts in graphs and hypergraphs"
    - "approximate undirected maximum flows in o m polylog n time"
    - "simple parallel and distributed algorithms for spectral graph sparsification"

Connected through hyperedge HE-5 (induced by source paper "spectral sparsification and regret minimization beyond matrix multiplicative updates"):
  with members:
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an efficient algorithm for unweighted spectral graph sparsification"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"
    - "near optimal algorithms for online matrix prediction"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "min max graph partitioning and small set expansion"

Connected through hyperedge HE-6 (induced by source paper "sparsified cholesky and multigrid solvers for connection laplacians"):
  with members:
    - "sparsified cholesky solvers for sdd linear systems"
    - "uniform sampling for matrix approximation"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "stable camera motion estimation using convex programming"
    - "phase retrieval with polarization"
    - "effective stiffness generalizing effective resistance sampling to finite element matrices"
    - "a nearly mlogn time solver for sdd linear systems"

Connected through hyperedge HE-7 (induced by source paper "sparsification of two variable valued csps"):
  with members:
    - "on sketching quadratic forms"
    - "sketching cuts in graphs and hypergraphs"
    - "the sketching complexity of graph cuts"
    - "a sketching algorithm for spectral graph sparsification"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "graph sparsification in the semi streaming model"
    - "twice ramanujan sparsifiers"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

Connected through hyperedge HE-8 (induced by source paper "the adversarial noise threshold for distributed protocols"):
  with members:
    - "maximal noise in interactive communication over erasure channels and channels with feedback"
    - "balancing communication for multi party interactive coding"
    - "interactive channel capacity revisited"
    - "optimal error rates for interactive coding i adaptivity and other settings"
    - "optimal error rates for interactive coding ii efficiency and list decoding"
    - "tree codes and a conjecture on exponential sums"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"
    - "twice ramanujan 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.DM(Discrete Mathematics)