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: "communication optimal distributed principal component analysis in the column partition model"
  Abstract: We study the Principal Component Analysis (PCA) problem in the distributed and streaming models of computation. Given a matrix $A \in R^{m \times n},$ a rank parameter $k < rank(A)$, and an accuracy parameter $0 < \epsilon < 1$, we want to output an $m \times k$ orthonormal matrix $U$ for which $$ || A - U U^T ||_F^2 \le \left(1 + \epsilon \right) \cdot || A - A_k||_F^2, $$ where $A_k \in R^{m \times n}$ is the best rank-$k$ approximation to $A$. #R##N#This paper provides improved algorithms for distributed PCA and streaming PCA.

Connected through hyperedge HE-1 (induced by source paper "effective sketching methods for value function approximation"):
  with members:
    - "efficient second order online learning by sketching"
    - "accelerated gradient temporal difference learning"
    - "unifying task specification in reinforcement learning"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "optimal principal component analysis in distributed and streaming models"
    - "on convergence of emphatic temporal difference learning"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "greedy column subset selection new bounds and distributed algorithms"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "greedy minimization of weakly supermodular set functions"
    - "lazier than lazy greedy"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "a fast greedy algorithm for generalized column subset selection"

Connected through hyperedge HE-3 (induced by source paper "distributed low rank approximation of implicit functions of a matrix"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed kernel principal component analysis"
    - "improved distributed principal component analysis"
    - "fastfood approximate kernel expansions in loglinear time"
    - "relative errors for deterministic low rank matrix approximations"
    - "principal component analysis and higher correlations for distributed data"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-4 (induced by source paper "a practical guide to randomized matrix computations with matlab implementations"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "the modified nystrom method theories algorithms and extension"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-5 (induced by source paper "distributed kernel principal component analysis"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"
    - "optimal cur matrix decompositions"
    - "improved distributed principal component analysis"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-6 (induced by source paper "communication efficient distributed kernel principal component analysis"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"
    - "optimal cur matrix decompositions"
    - "improved distributed principal component analysis"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-7 (induced by source paper "on the performance overhead tradeoff of distributed principal component analysis via data partitioning"):
  with members:
    - "optimal principal component analysis in distributed and streaming models"
    - "improved distributed principal component analysis"

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)