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: "smoothed analysis of the condition numbers and growth factors of matrices"
  Abstract: Let $\orig{A}$ be any matrix and let $A$ be a slight random perturbation of $\orig{A}$. We prove that it is unlikely that $A$ has large condition number. Using this result, we prove it is unlikely that $A$ has large growth factor under Gaussian elimination without pivoting. By combining these results, we bound the smoothed precision needed by Gaussian elimination without pivoting. Our results improve the average-case analysis of Gaussian elimination without pivoting performed by Yeung and Chan (SIAM J. Matrix Anal. Appl., 1997).

Connected through hyperedge HE-1 (induced by source paper "on the distance between two neural networks and the stability of learning"):
  with members:
    - "optimization for deep learning theory and algorithms"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "huggingface s transformers state of the art natural language processing"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-2 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "smoothed efficient algorithms and reductions for network coordination games"):
  with members:
    - "improving the smoothed complexity of flip for max cut problems"
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "efficient local search in coordination games on graphs"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"
    - "inapproximability of nash equilibrium"
    - "constant rank bimatrix games are ppad hard"

Connected through hyperedge HE-4 (induced by source paper "nash equilibrium in smoothed polynomial time for network coordination games"):
  with members:
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "local max cut in smoothed polynomial time"
    - "an empirical study on computing equilibria in polymatrix games"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability of nash equilibrium"
    - "constant rank bimatrix games are ppad hard"
    - "query complexity of approximate nash equilibria"
    - "bilinear games polynomial time algorithms for rank based subclasses"

Connected through hyperedge HE-5 (induced by source paper "the well tempered lasso"):
  with members:
    - "k means has polynomial smoothed complexity"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "approximation algorithms for ell_0 low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "algorithms for ell_p low rank approximation"
    - "input sparsity and hardness for robust subspace approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "a friendly smoothed analysis of the simplex method"):
  with members:
    - "smoothed analysis of the successive shortest path algorithm"
    - "on the shadow simplex method for curved polyhedra"
    - "geometric random edge"
    - "solving totally unimodular lps with the shadow vertex algorithm"
    - "the simplex method is strongly polynomial for deterministic markov decision processes"
    - "beyond hirsch conjecture walks on random polytopes and smoothed complexity of the simplex method"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-8 (induced by source paper "approximate spectral clustering efficiency and guarantees"):
  with members:
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning well clustered graphs with k means and heat kernel"
    - "partitioning into expanders"
    - "spectral clustering via the power method provably"
    - "a tutorial on spectral clustering"

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.NA(Numerical Analysis)