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: "implementing regularization implicitly via approximate eigenvector computation"
  Abstract: Regularization is a powerful technique for extracting useful information from noisy data. Typically, it is implemented by adding some sort of norm constraint to an objective function and then exactly optimizing the modified objective function. This procedure often leads to optimization problems that are computationally more expensive than the original problem, a fact that is clearly problematic if one is interested in large-scale applications. On the other hand, a large body of empirical work has demonstrated that heuristics, and in some cases approximation algorithms, developed to speed up computations sometimes have the side-effect of performing regularization implicitly. Thus, we consider the question: What is the regularized optimization objective that an approximation algorithm is exactly optimizing? #R##N#We address this question in the context of computing approximations to the smallest nontrivial eigenvector of a graph Laplacian; and we consider three random-walk-based procedures: one based on the heat kernel of the graph, one based on computing the the PageRank vector associated with the graph, and one based on a truncated lazy random walk. In each case, we provide a precise characterization of the manner in which the approximation method can be viewed as implicitly computing the exact solution to a regularized problem. Interestingly, the regularization is not on the usual vector form of the optimization problem, but instead it is on a related semidefinite program.

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 "implicit regularization of normalization methods"):
  with members:
    - "over parameterization as a catalyst for better generalization of deep relu network"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "a quantitative analysis of the effect of batch normalization on gradient descent"
    - "implicit regularization in deep matrix factorization"
    - "theoretical issues in deep networks approximation optimization and generalization"
    - "luck matters understanding training dynamics of deep relu networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "block models and personalized pagerank"):
  with members:
    - "community detection in networks with unequal groups"
    - "find your place simple distributed algorithms for community detection"
    - "heat kernel based community detection"
    - "streaming graph partitioning in the planted partition model"
    - "pagerank beyond the web"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"

Connected through hyperedge HE-5 (induced by source paper "self paced learning an implicit regularization perspective"):
  with members:
    - "what objective does self paced learning indeed optimize"
    - "a survey on multi view learning"
    - "approximate computation and implicit regularization for very large scale data analysis"

Connected through hyperedge HE-6 (induced by source paper "heat kernel based community detection"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "defining and evaluating network communities based on ground truth"
    - "google matrix dynamical attractors and ulam networks"
    - "fast incremental and personalized pagerank"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (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"
    - "regularized laplacian estimation and fast eigenvector approximation"
    - "the push algorithm for spectral ranking"
    - "localization on low order eigenvectors of data matrices"
    - "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-8 (induced by source paper "approximate computation and implicit regularization for very large scale data analysis"):
  with members:
    - "regularized laplacian estimation and fast eigenvector approximation"
    - "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"
    - "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"

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)