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: "note on sampling without replacing from a finite collection of matrices"
  Abstract: This technical note supplies an affirmative answer to a question raised in a recent pre-print [arXiv:0910.1879] in the context of a "matrix recovery" problem. Assume one samples m Hermitian matrices X_1, ..., X_m with replacement from a finite collection. The deviation of the sum X_1+...+X_m from its expected value in terms of the operator norm can be estimated by an "operator Chernoff-bound" due to Ahlswede and Winter. The question arose whether the bounds obtained this way continue to hold if the matrices are sampled without replacement. We remark that a positive answer is implied by a classical argument by Hoeffding. Some consequences for the matrix recovery problem are sketched.

Connected through hyperedge HE-1 (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"
    - "unbiased estimates for linear regression via volume sampling"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "low rank matrix completion in a general non orthogonal basis"):
  with members:
    - "exact reconstruction of euclidean distance geometry problem using low rank matrix completion"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "matrix completion on graphs"
    - "learning with the weighted trace norm under arbitrary sampling distributions"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "exact reconstruction of euclidean distance geometry problem using low rank matrix completion"):
  with members:
    - "euclidean distance matrices essential theory algorithms and applications"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"):
  with members:
    - "introduction to online convex optimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "a parallel sgd method with strong convergence"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "distributed learning communication complexity and privacy"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-5 (induced by source paper "incoherence optimal matrix completion"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral compressed sensing via structured matrix completion"
    - "hidden cliques and the certification of the restricted isometry property"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-6 (induced by source paper "exact tensor completion using t svd"):
  with members:
    - "adaptive sampling of rf fingerprints for fine grained indoor localization"
    - "incoherence optimal matrix completion"
    - "clustering multi way data a novel algebraic approach"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "a new convex relaxation for tensor completion"
    - "the power and arnoldi methods in an algebra of circulants"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-7 (induced by source paper "far field compression for fast kernel summation methods in high dimensions"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "matrix coherence and the nystrom method"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "optimized m2l kernels for the chebyshev interpolation based fast multipole method"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-8 (induced by source paper "ripless compressed sensing from anisotropic measurements"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "a probabilistic and ripless theory of compressed sensing"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

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.IT(Information Theory)