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: "an rip based approach to sigma delta quantization for compressed sensing"
  Abstract: In this letter, we provide a new approach to estimating the error of reconstruction from ΣΔ quantized compressed sensing measurements. Our method is based on the restricted isometry property (RIP) of a certain projection of the measurement matrix. Our result yields simple proofs and a slight generalization of the best-known reconstruction error bounds for Gaussian and subGaussian measurement matrices.

Connected through hyperedge HE-1 (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-2 (induced by source paper "quantized compressed sensing for partial random circulant matrices"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "quantization of compressive samples with stable and robust recovery"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "lower bounds for the error decay incurred by coarse quantization schemes"

Connected through hyperedge HE-3 (induced by source paper "quantization and compressive sensing"):
  with members:
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-4 (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"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "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-5 (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"
    - "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"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-6 (induced by source paper "noise shaping quantization methods for frame based and compressive sampling systems"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "quantization of compressive samples with stable and robust recovery"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "joint quantization and diffusion for compressed sensing measurements of natural images"):
  with members:
    - "distribution of compressive measurements generated by structurally random matrices"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "fast and efficient compressive sensing using structurally random 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)