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: "noise shaping quantization methods for frame based and compressive sampling systems"
  Abstract: Noise shaping refers to an analog-to-digital conversion methodology in which quantization error is arranged to lie mostly outside the signal spectrum by means of oversampling and feedback. Recently it has been successfully applied to more general redundant linear sampling and reconstruction systems associated with frames as well as non-linear systems associated with compressive sampling. This chapter reviews some of the recent progress in this subject.

Connected through hyperedge HE-1 (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"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"

Connected through hyperedge HE-2 (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"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "the recoverability limit for superresolution via sparsity"
    - "support detection in super resolution"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-3 (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"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "memoryless scalar quantization for random frames"):
  with members:
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "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"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-5 (induced by source paper "analysis of decimation on finite frames with sigma delta quantization"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"

Connected through hyperedge HE-6 (induced by source paper "from compressed sensing to compressed bit streams practical encoders tractable decoders"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "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 "quantization of compressive samples with stable and robust recovery"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "quantization and compressive sensing"
    - "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-8 (induced by source paper "sigma delta quantization with harmonic frames and partial fourier ensembles"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "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"
    - "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"

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)