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: "lower bounds for sparse recovery"
  Abstract: We consider the following k-sparse recovery problem: design an m x n matrix A, such that for any signal x, given Ax we can efficiently recover x' satisfying #R##N#||x-x'||_1 <= C min_{k-sparse} x"} ||x-x"||_1. #R##N#It is known that there exist matrices A with this property that have only O(k log (n/k)) rows. #R##N#In this paper we show that this bound is tight. Our bound holds even for the more general /randomized/ version of the problem, where A is a random variable and the recovery algorithm is required to work for any fixed x with constant probability (over A).

Connected through hyperedge HE-1 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "on estimating maximum matching size in graph streams"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-2 (induced by source paper "tensor regression using low rank and sparse tucker decompositions"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "learning from multiway data simple and efficient tensor regression"
    - "low rank tensor recovery via iterative hard thresholding"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
    - "guaranteed rank minimization via singular value projection"
    - "most tensor problems are np hard"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "testing noisy linear functions for sparsity"):
  with members:
    - "almost optimal distribution free junta testing"
    - "estimating learnability in the sublinear data regime"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "testing low complexity affine invariant properties"
    - "a unified framework for testing linear invariant properties"

Connected through hyperedge HE-4 (induced by source paper "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "discrete uncertainty principles and sparse signal processing"
    - "sparsity lower bounds for dimensionality reducing maps"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for compressed sensing with generative models"):
  with members:
    - "information theoretic lower bounds for compressive sensing with generative models"

Connected through hyperedge HE-6 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "maximum matching in turnstile streams"
    - "stream sampling for frequency cap statistics"
    - "structure aware sampling flexible and accurate summarization"

Connected through hyperedge HE-7 (induced by source paper "batch sparse recovery or how to leverage the average sparsity"):
  with members:
    - "when distributed computation is communication expensive"
    - "lower bounds for adaptive sparse recovery"
    - "on the power of adaptivity in sparse recovery"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-8 (induced by source paper "sublinear time algorithms for compressive phase retrieval"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "predicting positive and negative links with noisy queries theory practice"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"

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)