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: "near optimal coresets for least squares regression"
  Abstract: We study the (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low-order polynomial-time algorithms to construct such coresets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results.

Connected through hyperedge HE-1 (induced by source paper "coresets for gaussian mixture models of any shape"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
    - "learning mixture of gaussians with streaming data"
    - "on the k means median cost function"
    - "secure search on the cloud via coresets and sketches"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-2 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-3 (induced by source paper "reverse iterative volume sampling for linear regression"):
  with members:
    - "active regression via linear sample sparsification"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "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-4 (induced by source paper "active regression via linear sample sparsification"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "tail bounds for volume sampled linear regression"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "fourier sparse interpolation without a frequency gap"
    - "convergence rates of active learning for maximum likelihood estimation"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "linear regression without correspondence"):
  with members:
    - "unbiased estimates for linear regression via volume sampling"
    - "unlabeled sensing with random linear measurements"

Connected through hyperedge HE-6 (induced by source paper "coresets for kernel regression"):
  with members:
    - "coresets and sketches"
    - "efficient density estimation via piecewise polynomial approximation"

Connected through hyperedge HE-7 (induced by source paper "column selection via adaptive sampling"):
  with members:
    - "optimal sparse linear auto encoders and sparse pca"
    - "provable deterministic leverage score sampling"
    - "a note on sparse least squares regression"
    - "deterministic feature selection for k means clustering"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "efficient volume sampling for row column subset selection"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-8 (induced by source paper "dimmwitted a study of main memory statistical analytics"):
  with members:
    - "an approximate efficient solver for lp rounding"
    - "mli an api for distributed machine learning"
    - "massively parallel sort merge joins in main memory multi core database systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "building high level features using large scale unsupervised learning"
    - "a reliable effective terascale linear learning system"
    - "measuring numa effects with the stream benchmark"

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)