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: "condition numbers of gaussian random matrices"
  Abstract: Let $G_{m \times n}$ be an $m \times n$ real random matrix whose elements are independent and identically distributed standard normal random variables, and let $\kappa_2(G_{m \times n})$ be the 2-norm condition number of $G_{m \times n}$. We prove that, for any $m \geq 2$, $n \geq 2$ and $x \geq |n-m|+1$, $\kappa_2(G_{m \times n})$ satisfies $ #R##N#\frac{1}{\sqrt{2\pi}} ({c}/{x})^{|n-m|+1}   x) < #R##N#\frac{1}{\sqrt{2\pi}} ({C}/{x})^{|n-m|+1}, $ where $0.245 \leq c \leq 2.000$ and $ 5.013 \leq C \leq 6.414$ are universal positive constants independent of $m$, $n$ and $x$. Moreover, for any $m \geq 2$ and $n \geq 2$, $ E(\log\kappa_2(G_{m \times n})) < \log \frac{n}{|n-m|+1} + 2.258. $ A similar pair of results for complex Gaussian random matrices is also established.

Connected through hyperedge HE-1 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "accelerating sgd with momentum for over parameterized learning"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "double descent in the condition number"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-2 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior"):
  with members:
    - "inference under information constraints i lower bounds from chi square contraction"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "on lightweight privacy preserving collaborative learning for internet of things objects"):
  with members:
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "learning differentially private recurrent language models"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "improving the robustness of deep neural networks via stability training"
    - "crowd ml a privacy preserving learning framework for a crowd of smart devices"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "regularized weighted low rank approximation"):
  with members:
    - "practical sketching algorithms for low rank matrix approximation"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "double descent in the condition number"):
  with members:
    - "two models of double descent for weak features"

Connected through hyperedge HE-6 (induced by source paper "image reconstruction using matched wavelet estimated from data sensed compressively using partial canonical identity matrix"):
  with members:
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-7 (induced by source paper "local sparsity and recovery of fusion frames structured signals"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on partial sparse recovery"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "sparse recovery from combined fusion frame measurements"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-8 (induced by source paper "block compressed sensing based distributed device detection for m2m communications"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "newton like method with diagonal correction for distributed optimization"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "iterative hard thresholding for compressed sensing"

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)