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: "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
  Abstract: This letter focuses on the estimation of low-complexity signals when they are observed through   $M$   uniformly quantized compressive observations. Among such signals, we consider 1-D sparse vectors, low-rank matrices, or compressible signals that are well approximated by one of these two models. In this context, we prove the estimation efficiency of a variant of Basis Pursuit Denoise, called Consistent Basis Pursuit (CoBP), enforcing consistency between the observations and the re-observed estimate, while promoting its low-complexity nature. We show that the reconstruction error of CoBP decays like   ${M^{ - 1/4}}$   when all parameters but   $M$    are fixed. Our proof is connected to recent bounds on the proximity of vectors or matrices when (i) those belong to a set of small intrinsic “dimension”, as measured by the Gaussian mean width, and (ii) they share the same quantized (dithered) random projections. By solving CoBP with a proximal algorithm, we provide some extensive numerical observations that confirm the theoretical bound as   $M$   is increased, displaying even faster error decay than predicted. The same phenomenon is observed in the special, yet important case of 1-bit CS.

Connected through hyperedge HE-1 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "sketching for large scale learning of mixture models"

Connected through hyperedge HE-2 (induced by source paper "one bit compressed sensing with partial gaussian circulant matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "on the gap between rip properties and sparse recovery conditions"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"

Connected through hyperedge HE-3 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "bounds on the number of measurements for reliable compressive classification"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "quantization and compressive sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-4 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"

Connected through hyperedge HE-6 (induced by source paper "from compressed sensing to compressed bit streams practical encoders tractable decoders"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "one bit compressed sensing with non gaussian measurements"

Connected through hyperedge HE-7 (induced by source paper "optimizing quantization for lasso recovery"):
  with members:
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "quantization and compressive sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "message passing de quantization with applications to compressed sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "frame permutation quantization"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

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)