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: "much faster algorithms for matrix scaling"
  Abstract: We develop several efficient algorithms for the classical \emph{Matrix Scaling} problem, which is used in many diverse areas, from preconditioning linear systems to approximation of the permanent. On an input $n\times n$ matrix $A$, this problem asks to find diagonal (scaling) matrices $X$ and $Y$ (if they exist), so that $X A Y$ $\varepsilon$-approximates a doubly stochastic, or more generally a matrix with prescribed row and column sums. #R##N#We address the general scaling problem as well as some important special cases. In particular, if $A$ has $m$ nonzero entries, and if there exist $X$ and $Y$ with polynomially large entries such that $X A Y$ is doubly stochastic, then we can solve the problem in total complexity $\tilde{O}(m + n^{4/3})$. This greatly improves on the best known previous results, which were either $\tilde{O}(n^4)$ or $O(m n^{1/2}/\varepsilon)$. #R##N#Our algorithms are based on tailor-made first and second order techniques, combined with other recent advances in continuous optimization, which may be of independent interest for solving similar problems.

Connected through hyperedge HE-1 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "computing maximum flow with augmenting electrical flows"

Connected through hyperedge HE-2 (induced by source paper "iterative refinement for ell_p norm regression"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "efficient inverse maintenance and faster algorithms for linear programming"

Connected through hyperedge HE-3 (induced by source paper "faster p norm minimizing flows via smoothed q norm problems"):
  with members:
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "flows in almost linear time via adaptive preconditioning"
    - "coordinate methods for accelerating ell_ infty regression and faster approximate maximum flow"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"

Connected through hyperedge HE-4 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "a graph theoretic additive approximation of optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "approximating optimal transport with linear programs"
    - "approximating the quadratic transportation metric in near linear time"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"

Connected through hyperedge HE-5 (induced by source paper "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "approximating optimal transport with linear programs"
    - "sparsemax and relaxed wasserstein for topic sparsity"
    - "probabilistic multilevel clustering via composite transportation distance"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "sliced wasserstein kernel for persistence diagrams"

Connected through hyperedge HE-6 (induced by source paper "fair distributions from biased samples a maximum entropy optimization framework"):
  with members:
    - "bagan data augmentation with balancing gan"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "fairgan fairness aware generative adversarial networks"
    - "maximum entropy distributions bit complexity and stability"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-7 (induced by source paper "spectral analysis of matrix scaling and operator scaling"):
  with members:
    - "operator scaling via geodesically convex optimization invariant theory and polynomial identity testing"
    - "testing halfspaces over rotation invariant distributions"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "operator scaling theory and applications"
    - "a deterministic polynomial time algorithm for non commutative rational identity testing"
    - "algorithms and hardness for robust subspace recovery"
    - "perfect matchings in o n 1 5 time in regular bipartite graphs"

Connected through hyperedge HE-8 (induced by source paper "autoshufflenet learning permutation matrices via an exact lipschitz continuous penalty in deep convolutional neural networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "convergence analysis of two layer neural networks with relu activation"
    - "a path following algorithm for the graph matching problem"

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)