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: "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
  Abstract: This paper introduces a new algorithm for the so-called “analysis problem” in quantization of finite frame representations that provides a near-optimal solution in the case of random measurements. The main contributions include the development of a general quantization framework called distributed noise shaping and, in particular, beta duals of frames, as well as the performance analysis of beta duals in both deterministic and probabilistic settings. It is shown that for random frames, using beta duals results in near-optimally accurate reconstructions with respect to both the frame redundancy and the number of levels at which the frame coefficients are quantized. More specifically, for any frame E of m vectors in \(\mathbb {R}^k\) except possibly from a subset of Gaussian measure exponentially small in m and for any number \(L \ge 2\) of quantization levels per measurement to be used to encode the unit ball in \(\mathbb {R}^k\), there is an algorithmic quantization scheme and a dual frame together that guarantee a reconstruction error of at most \(\sqrt{k}L^{-(1-\eta )m/k}\), where \(\eta \) can be arbitrarily small for sufficiently large problems. Additional features of the proposed algorithm include low computational cost and parallel implementability.

Connected through hyperedge HE-1 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds"):
  with members:
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "on recovery guarantees for one bit compressed sensing on manifolds"
    - "quantization for low rank matrix recovery"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "quantization and compressive sensing"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "adapted decimation on finite frames for arbitrary orders of sigma delta quantization"):
  with members:
    - "analysis of decimation on finite frames with sigma delta quantization"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"

Connected through hyperedge HE-3 (induced by source paper "high performance quantization for spectral super resolution"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "the recoverability limit for superresolution via sparsity"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "support detection in super resolution"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-4 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"

Connected through hyperedge HE-5 (induced by source paper "analysis of decimation on finite frames with sigma delta quantization"):
  with members:
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "noise shaping quantization methods for frame based and compressive sampling systems"

Connected through hyperedge HE-6 (induced by source paper "quantization of compressive samples with stable and robust recovery"):
  with members:
    - "quantization and compressive sensing"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "message passing de quantization with applications to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "sigma delta quantization with harmonic frames and partial fourier ensembles"):
  with members:
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "the restricted isometry property of subsampled fourier matrices"
    - "quantization of compressive samples with stable and robust recovery"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-8 (induced by source paper "noise shaping quantization methods for frame based and compressive sampling systems"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "lower bounds for the error decay incurred by coarse quantization schemes"
    - "iterative hard thresholding for compressed sensing"

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)