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: "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
  Abstract: Suppose that the collection $\{e_i\}_{i=1}^m$ forms a frame for $\R^k$, where each entry of the vector $e_i$ is a sub-Gaussian random variable. We consider expansions in such a frame, which are then quantized using a Sigma-Delta scheme. We show that an arbitrary signal in $\R^k$ can be recovered from its quantized frame coefficients up to an error which decays root-exponentially in the oversampling rate $m/k$. Here the quantization scheme is assumed to be chosen appropriately depending on the oversampling rate and the quantization alphabet can be coarse. The result holds with high probability on the draw of the frame uniformly for all signals. The crux of the argument is a bound on the extreme singular values of the product of a deterministic matrix and a sub-Gaussian frame. For fine quantization alphabets, we leverage this bound to show polynomial error decay in the context of compressed sensing. Our results extend previous results for structured deterministic frame expansions and Gaussian compressed sensing measurements.

Connected through hyperedge HE-1 (induced by source paper "on one stage recovery for sigma delta quantized compressed sensing"):
  with members:
    - "memoryless scalar quantization for random frames"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"
    - "quantization of compressive samples with stable and robust recovery"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"
    - "lower bounds for the error decay incurred by coarse quantization schemes"

Connected through hyperedge HE-2 (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-3 (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"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-4 (induced by source paper "feedback acquisition and reconstruction of spectrum sparse signals by predictive level comparisons"):
  with members:
    - "fast methods for recovering sparse parameters in linear low rank models"
    - "iterative methods for sparse signal reconstruction from level crossings"
    - "quantization of compressive samples with stable and robust recovery"
    - "sparse signal estimation by maximally sparse convex optimization"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "regime change bit depth versus measurement rate in compressive sensing"

Connected through hyperedge HE-5 (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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "lower bounds for the error decay incurred by coarse quantization schemes"

Connected through hyperedge HE-6 (induced by source paper "quantization for low rank matrix recovery"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "quantization and compressive sensing"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (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"
    - "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"
    - "one bit compressed sensing with non gaussian measurements"

Connected through hyperedge HE-8 (induced by source paper "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"):
  with members:
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"

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)