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: "improved distributed principal component analysis"
  Abstract: We study the distributed computing setting in which there are multiple servers, each holding a set of points, who wish to compute functions on the union of their point sets. A key task in this setting is Principal Component Analysis (PCA), in which the servers would like to compute a low dimensional subspace capturing as much of the variance of the union of their point sets as possible. Given a procedure for approximate PCA, one can use it to approximately solve $\ell_2$-error fitting problems such as $k$-means clustering and subspace clustering. The essential properties of an approximate distributed PCA algorithm are its communication cost and computational efficiency for a given desired accuracy in downstream applications. We give new algorithms and analyses for distributed PCA which lead to improved communication and computational costs for $k$-means clustering and related problems. Our empirical study on real world data shows a speedup of orders of magnitude, preserving communication with only a negligible degradation in solution quality. Some of these techniques we develop, such as a general transformation from a constant success probability subspace embedding to a high success probability subspace embedding with a dimension and sparsity independent of the success probability, may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "the communication complexity of optimization"):
  with members:
    - "distributed and streaming linear programming in low dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"
    - "distributed statistical estimation of matrix products with applications"
    - "l1 regression using lewis weights preconditioning and stochastic gradient descent"
    - "on communication complexity of classification problems"
    - "communication optimal distributed clustering"
    - "distributed low rank approximation of implicit functions of a matrix"

Connected through hyperedge HE-2 (induced by source paper "communication and memory efficient testing of discrete distributions"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "two party distribution testing communication and security"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "near optimal closeness testing of discrete histogram distributions"
    - "sample optimal identity testing with high probability"
    - "sharp bounds for generalized uniformity testing"
    - "generalized uniformity testing"
    - "testing conditional independence of discrete distributions"

Connected through hyperedge HE-3 (induced by source paper "reliable distributed clustering with redundant data assignment"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "distributed statistical estimation of matrix products with applications"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "worst case optimal algorithms for parallel query processing"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "on the complexity of inner product similarity join"
    - "communication efficient distributed kernel principal component analysis"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "skew in parallel query processing"

Connected through hyperedge HE-5 (induced by source paper "a practical algorithm for distributed clustering and outlier detection"):
  with members:
    - "distributed partial clustering"
    - "communication optimal distributed clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "optimal time bounds for approximate clustering"
    - "scalable k means"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-6 (induced by source paper "tight bounds for collaborative pac learning via multiplicative weights"):
  with members:
    - "improved algorithms for collaborative pac learning"
    - "distributed partial clustering"
    - "the optimal sample complexity of pac learning"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-7 (induced by source paper "faster matrix completion using randomized svd"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "fast randomized pca for sparse data"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"

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)