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: "regularized laplacian estimation and fast eigenvector approximation"
  Abstract: Recently, Mahoney and Orecchia demonstrated that popular diffusion-based procedures to compute a quick \emph{approximation} to the first nontrivial eigenvector of a data graph Laplacian \emph{exactly} solve certain regularized Semi-Definite Programs (SDPs). In this paper, we extend that result by providing a statistical interpretation of their approximation procedure. Our interpretation will be analogous to the manner in which $\ell_2$-regularized or $\ell_1$-regularized $\ell_2$-regression (often called Ridge regression and Lasso regression, respectively) can be interpreted in terms of a Gaussian prior or a Laplace prior, respectively, on the coefficient vector of the regression problem. Our framework will imply that the solutions to the Mahoney-Orecchia regularized SDP can be interpreted as regularized estimates of the pseudoinverse of the graph Laplacian. Conversely, it will imply that the solution to this regularized estimation problem can be computed very quickly by running, e.g., the fast diffusion-based PageRank procedure for computing an approximation to the first nontrivial eigenvector of the graph Laplacian. Empirical results are also provided to illustrate the manner in which approximate eigenvector computation \emph{implicitly} performs statistical regularization, relative to running the corresponding exact algorithm.

Connected through hyperedge HE-1 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "implicit regularization in deep matrix factorization"
    - "bayesian experimental design using regularized determinantal point processes"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"

Connected through hyperedge HE-2 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-3 (induced by source paper "semi supervised eigenvectors for large scale locally biased learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "the push algorithm for spectral ranking"
    - "localization on low order eigenvectors of data matrices"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "approximate computation and implicit regularization for very large scale data analysis"):
  with members:
    - "the push algorithm for spectral ranking"
    - "randomized algorithms for matrices and data"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "empirical comparison of algorithms for network community detection"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "fast incremental and personalized pagerank"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-5 (induced by source paper "impact of dynamic interactions on multi scale analysis of community structure in networks"):
  with members:
    - "social structure of facebook networks"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "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.DS(Data Structures and Algorithms)