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: "1 eps approximate sparse recovery"
  Abstract: The problem central to sparse recovery and compressive sensing is that of stable sparse recovery: we want a distribution of matrices A in R^{m\times n} such that, for any x \in R^n and with probability at least 2/3 over A, there is an algorithm to recover x* from Ax with #R##N#||x* - x||_p   1 and norm p. The measurement complexity of this problem is well understood for constant C > 1. However, in a variety of applications it is important to obtain C = 1 + eps for a small eps > 0, and this complexity is not well understood. We resolve the dependence on eps in the number of measurements required of a k-sparse recovery algorithm, up to polylogarithmic factors for the central cases of p = 1 and p = 2. Namely, we give new algorithms and lower bounds that show the number of measurements required is (1/eps^{p/2})k polylog(n). For p = 2, our bound of (1/eps) k log(n/k) is tight up to constant factors. We also give matching bounds when the output is required to be k-sparse, in which case we achieve (1/eps^p) k polylog(n). This shows the distinction between the complexity of sparse and non-sparse outputs is fundamental.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (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"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-3 (induced by source paper "improved algorithms for adaptive compressed sensing"):
  with members:
    - "sublinear time algorithms for compressive phase retrieval"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "lower bounds for adaptive sparse recovery"
    - "on the power of adaptivity in sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"
    - "sequential adaptive compressed sampling via huffman codes"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "almost optimal phaseless compressed sensing with sublinear decoding time"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "heavy hitters via cluster preserving clustering"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "super sparse signals with unknown phases efficiently recovered"

Connected through hyperedge HE-6 (induced by source paper "on low risk heavy hitters and sparse recovery schemes"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "clustering high dimensional dynamic data streams"
    - "heavy hitters via cluster preserving clustering"
    - "restricted isometry property for general p norms"
    - "for all sparse recovery in near optimal time"
    - "compressive sensing with local geometric features"
    - "on deterministic sketching and streaming for sparse recovery and norm estimation"
    - "on the power of adaptivity in sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "nearly optimal sparse fourier transform"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "agile millimeter wave networks with provable guarantees"):
  with members:
    - "phase retrieval an overview of recent developments"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "nearly optimal sparse fourier transform"
    - "coding the beams improving beamforming training in mmwave communication system"
    - "lower bounds for sparse recovery"
    - "in building wideband partition loss measurements at 2 5 and 60 ghz"

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)