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: "quantile regression for large scale applications"
  Abstract: Quantile regression is a method to estimate the quantiles of the conditional distribution of a response variable, and as such it permits a much more accurate portrayal of the relationship between the response variable and observed covariates than methods such as Least-squares or Least Absolute Deviations regression. It can be expressed as a linear program, and, with appropriate preprocessing, interior-point methods can be used to find a solution for moderately large problems. Dealing with very large problems, \emph(e.g.), involving data up to and beyond the terabyte regime, remains a challenge. Here, we present a randomized algorithm that runs in nearly linear time in the size of the input and that, with constant probability, computes a $(1+\epsilon)$ approximate solution to an arbitrary quantile regression problem. As a key step, our algorithm computes a low-distortion subspace-preserving embedding with respect to the loss function of quantile regression. Our empirical evaluation illustrates that our algorithm is competitive with the best previous work on small to medium-sized problems, and that in addition it can be implemented in MapReduce-like environments and applied to terabyte-sized problems.

Connected through hyperedge HE-1 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-2 (induced by source paper "dimensionality reduction for tukey regression"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "input sparsity and hardness for robust subspace approximation"
    - "robust optimization for deep regression"
    - "sketching as a tool for numerical linear algebra"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-3 (induced by source paper "tight bounds for the subspace sketch problem with applications"):
  with members:
    - "distributed statistical estimation of matrix products with applications"
    - "high probability frequency moment sketches"
    - "optimal lower bounds for sketching graph cuts"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "on sketching quadratic forms"
    - "sketching as a tool for numerical linear algebra"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "the fast cauchy transform and faster robust linear regression"

Connected through hyperedge HE-4 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks"):
  with members:
    - "fast and rip optimal transforms"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-5 (induced by source paper "tight bounds for ell_p oblivious subspace embeddings"):
  with members:
    - "uniform sampling for matrix approximation"
    - "lower bounds for oblivious subspace embeddings"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-6 (induced by source paper "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "relative errors for deterministic low rank matrix approximations"
    - "how robust are linear sketches to adaptive inputs"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "on the complexity of processing massive unordered distributed data"

Connected through hyperedge HE-7 (induced by source paper "implementing randomized matrix algorithms in parallel and distributed environments"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-8 (induced by source paper "subspace embeddings and ell_p regression using exponential random variables"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"
    - "randomized algorithms for matrices and data"
    - "sparser johnson lindenstrauss transforms"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"

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)