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: "solving linear programs in the current matrix multiplication time"
  Abstract: This paper shows how to solve linear programs of the form $\min_{Ax=b,x\geq0} c^\top x$ with $n$ variables in time $$O^*((n^{\omega}+n^{2.5-\alpha/2}+n^{2+1/6}) \log(n/\delta))$$ where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. For the current value of $\omega\sim2.37$ and $\alpha\sim0.31$, our algorithm takes $O^*(n^{\omega} \log(n/\delta))$ time. When $\omega = 2$, our algorithm takes $O^*(n^{2+1/6} \log(n/\delta))$ time. #R##N#Our algorithm utilizes several new concepts that we believe may be of independent interest: #R##N#$\bullet$ We define a stochastic central path method. #R##N#$\bullet$ We show how to maintain a projection matrix $\sqrt{W}A^{T}(AWA^{\top})^{-1}A\sqrt{W}$ in sub-quadratic time under $\ell_{2}$ multiplicative changes in the diagonal matrix $W$.

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"
    - "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"
    - "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 "faster divergence maximization for faster maximum flow"):
  with members:
    - "circulation control for faster minimum cost flow in unit capacity graphs"
    - "solving tall dense linear programs in nearly linear time"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "solving linear programs with sqrt rank linear system solves"
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"

Connected through hyperedge HE-3 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "from group recommendations to group formation"
    - "when social influence meets item inference"

Connected through hyperedge HE-4 (induced by source paper "speeding up linear programming using randomized linear algebra"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "solving linear programs with sqrt rank linear system solves"
    - "optimal approximate matrix product in terms of stable rank"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-5 (induced by source paper "barriers for rectangular matrix multiplication"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "limits on the universal method for matrix multiplication"
    - "barriers for fast matrix multiplication from irreversibility"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "further limitations of the known approaches for matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-7 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "communication efficient distributed kernel principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "on fast decoding of high dimensional signals from one bit measurements"

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)