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: "stability of the lanczos method for matrix function approximation"
  Abstract: The ubiquitous Lanczos method can approximate $f(A)x$ for any symmetric $n \times n$ matrix $A$, vector $x$, and function $f$. In exact arithmetic, the method's error after $k$ iterations is bounded by the error of the best degree-$k$ polynomial uniformly approximating $f(x)$ on the range $[\lambda_{min}(A), \lambda_{max}(A)]$. However, despite decades of work, it has been unclear if this powerful guarantee holds in finite precision. #R##N#We resolve this problem, proving that when $\max_{x \in [\lambda_{min}, \lambda_{max}]}|f(x)| \le C$, Lanczos essentially matches the exact arithmetic guarantee if computations use roughly $\log(nC\|A\|)$ bits of precision. Our proof extends work of Druskin and Knizhnerman [DK91], leveraging the stability of the classic Chebyshev recurrence to bound the stability of any polynomial approximating $f(x)$. #R##N#We also study the special case of $f(A) = A^{-1}$, where stronger guarantees hold. In exact arithmetic Lanczos performs as well as the best polynomial approximating $1/x$ at each of $A$'s eigenvalues, rather than on the full eigenvalue range. In seminal work, Greenbaum gives an approach to extending this bound to finite precision: she proves that finite precision Lanczos and the related CG method match any polynomial approximating $1/x$ in a tiny range around each eigenvalue [Gre89]. #R##N#For $A^{-1}$, this bound appears stronger than ours. However, we exhibit matrices with condition number $\kappa$ where exact arithmetic Lanczos converges in $polylog(\kappa)$ iterations, but Greenbaum's bound predicts $\Omega(\kappa^{1/5})$ iterations. It thus cannot offer significant improvement over the $O(\kappa^{1/2})$ bound achievable via our result. Our analysis raises the question of if convergence in less than $poly(\kappa)$ iterations can be expected in finite precision, even for matrices with clustered, skewed, or otherwise favorable eigenvalue distributions.

Connected through hyperedge HE-1 (induced by source paper "break the ceiling stronger multi scale deep graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "virtual adversarial training on graph convolutional networks in node classification"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph partition neural networks for semi supervised classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-2 (induced by source paper "a rank 1 sketch for matrix multiplicative weights"):
  with members:
    - "introduction to online convex optimization"
    - "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "geometric median in nearly linear time"
    - "faster and simpler width independent parallel algorithms for positive semidefinite programming"

Connected through hyperedge HE-3 (induced by source paper "principal component projection and regression in nearly linear time through asymmetric svrg"):
  with members:
    - "stochastic variance reduction methods for saddle point problems"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "principal component projection without principal component analysis"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-4 (induced by source paper "perron frobenius theory in nearly linear time positive eigenvectors m matrices graph kernels and other applications"):
  with members:
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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)