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: "reverse iterative volume sampling for linear regression"
  Abstract: We study the following basic machine learning task: Given a fixed set of $d$-dimensional input points for a linear regression problem, we wish to predict a hidden response value for each of the points. We can only afford to attain the responses for a small subset of the points that are then used to construct linear predictions for all points in the dataset. The performance of the predictions is evaluated by the total square loss on all responses (the attained as well as the hidden ones). We show that a good approximate solution to this least squares problem can be obtained from just dimension $d$ many responses by using a joint sampling technique called volume sampling. Moreover, the least squares solution obtained for the volume sampled subproblem is an unbiased estimator of optimal solution based on all n responses. This unbiasedness is a desirable property that is not shared by other common subset selection techniques. #R##N#Motivated by these basic properties, we develop a theoretical framework for studying volume sampling, resulting in a number of new matrix expectation equalities and statistical guarantees which are of importance not only to least squares regression but also to numerical linear algebra in general. Our methods also lead to a regularized variant of volume sampling, and we propose the first efficient algorithms for volume sampling which make this technique a practical tool in the machine learning toolbox. Finally, we provide experimental evidence which confirms our theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "two models of double descent for weak features"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "greedy column subset selection new bounds and distributed algorithms"

Connected through hyperedge HE-2 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "adversarial active learning for deep networks a margin based approach"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "libact pool based active learning in python"
    - "determinantal point processes for mini batch diversification"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"

Connected through hyperedge HE-3 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "guarantees for greedy maximization of non submodular functions with applications"

Connected through hyperedge HE-4 (induced by source paper "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"):
  with members:
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-5 (induced by source paper "distributed estimation of the inverse hessian by determinantal averaging"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "leveraged volume sampling for linear regression"
    - "giant globally improved approximate newton method for distributed optimization"
    - "unbiased estimates for linear regression via volume sampling"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "federated optimization distributed machine learning for on device intelligence"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-6 (induced by source paper "subset selection for matrices with fixed blocks"):
  with members:
    - "discrete signal processing on graphs sampling theory"
    - "powers of tensors and fast matrix multiplication"
    - "deterministic feature selection for k means clustering"
    - "faster subset selection for matrices and applications"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-7 (induced by source paper "fast determinantal point processes via distortion free intermediate sampling"):
  with members:
    - "leveraged volume sampling for linear regression"
    - "efficient sampling for k determinantal point processes"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-8 (induced by source paper "correcting the bias in least squares regression with volume rescaled sampling"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "leveraged volume sampling for linear regression"
    - "how to be fair and diverse"
    - "optimal column based low rank matrix reconstruction"

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.LG(Machine Learning)