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: "unbiased estimates for linear regression via volume sampling"
  Abstract: For a full rank $n\times d$ matrix $X$ with $n\ge d$, consider the task of solving the linear least squares problem, where we try to predict a response value for each of the $n$ rows of $X$. Assume that obtaining the responses is expensive and we can only afford to attain the responses for a small subset of rows. We show that a good approximate solution to this least squares problem can be obtained from just dimension $d$ many responses. Concretely, if the rows are in general position and if a subset of $d$ rows is chosen proportional to the squared volume spanned by those rows, then the expected total square loss (on all $n$ rows) of the least squares solution found for the subset is exactly $d+1$ times the minimum achievable total loss. We provide lower bounds showing that the factor of $d+1$ is optimal, and any iid row sampling procedure requires $\Omega(d\log d)$ responses to achieve a finite factor guarantee. Moreover, the least squares solution obtained for the volume sampled subproblem is an unbiased estimator of optimal solution based on all $n$ responses. #R##N#Our methods lead to general matrix expectation formulas for volume sampling which go beyond linear regression. In particular, we propose a matrix estimator for the pseudoinverse $X^+$, computed from a small subset of rows of the matrix $X$. The estimator is unbiased and surprisingly its covariance also has a closed form: It equals a specific factor times $X^{+}X^{+\top}$. We believe that these new formulas establish a fundamental connection between linear least squares and volume sampling. Our analysis for computing matrix expectations is based on reverse iterative volume sampling, a technique which also leads to a new algorithm for volume sampling that is by a factor of $n^2$ faster than the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"

Connected through hyperedge HE-2 (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-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"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"

Connected through hyperedge HE-4 (induced by source paper "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "fair and diverse dpp based data summarization"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "gdpp learning diverse generations using determinantal point process"
    - "guess free maximization of submodular and linear sums"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"
    - "diversity networks neural network compression using determinantal point processes"

Connected through hyperedge HE-5 (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"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "subsampling for ridge regression via regularized volume sampling"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-6 (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"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "giant globally improved approximate newton method for distributed optimization"
    - "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-7 (induced by source paper "reverse iterative volume sampling for linear regression"):
  with members:
    - "active regression via linear sample sparsification"
    - "subsampling for ridge regression via regularized volume sampling"
    - "near optimal coresets for least squares regression"
    - "low rank approximation and regression in input sparsity time"
    - "faster subset selection for matrices and applications"
    - "fast approximation of matrix coherence and statistical leverage"
    - "optimal column based low rank matrix reconstruction"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-8 (induced by source paper "tail bounds for volume sampled linear regression"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "relative error tensor low rank approximation"
    - "subsampling for ridge regression via regularized volume sampling"
    - "how to be fair and diverse"
    - "sketching as a tool for numerical linear algebra"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "randomized algorithms for matrices and data"
    - "note on sampling without replacing from a finite collection of matrices"

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)