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: "ripless compressed sensing from anisotropic measurements"
  Abstract: Abstract   Compressed sensing is the art of reconstructing a sparse vector from its inner products with respect to a small set of randomly chosen measurement vectors. It is usually assumed that the ensemble of measurement vectors is in  isotropic position  in the sense that the associated covariance matrix is proportional to the identity matrix. In this paper, we establish bounds on the number of required measurements in the  anisotropic  case, where the ensemble of measurement vectors possesses a non-trivial covariance matrix. Essentially, we find that the required sampling rate grows proportionally to the condition number of the covariance matrix. In contrast to other recent contributions to this problem, our arguments do not rely on any  restricted isometry properties  (RIP’s), but rather on ideas from convex geometry which have been systematically studied in the theory of low-rank matrix recovery. This allows for a simple argument and slightly improved bounds, but may lead to a worse dependency on noise (which we do not consider in the present paper).

Connected through hyperedge HE-1 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"):
  with members:
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "analyzing the structure of multidimensional compressed sensing problems through coherence"
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "sampling in the analysis transform domain"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "anisotropic compressed sensing for non cartesian mri acquisitions"):
  with members:
    - "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "improved recovery guarantees for phase retrieval from coded diffraction patterns"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "low rank matrix recovery from rank one measurements"
    - "phase retrieval from low rate samples"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-4 (induced by source paper "unified theory for recovery of sparse signals in a general transform domain"):
  with members:
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "robust spectral compressed sensing via structured matrix completion"
    - "living on the edge phase transitions in convex programs with random data"
    - "guarantees of total variation minimization for signal recovery"
    - "oblique pursuits for compressed sensing"
    - "stable image reconstruction using total variation minimization"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-5 (induced by source paper "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "low rank matrix recovery from rank one measurements"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-6 (induced by source paper "bi level protected compressive sampling"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a probabilistic and ripless theory of compressed sensing"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"):
  with members:
    - "identifiability scaling laws in bilinear inverse problems"
    - "on the stability of sparse convolutions"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "phase retrieval stability and recovery guarantees"

Connected through hyperedge HE-8 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "complete dictionary recovery over the sphere"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "more algorithms for provable dictionary learning"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "on iterative hard thresholding methods for high dimensional m estimation"

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.IT(Information Theory)