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: "from compressed sensing to compressed bit streams practical encoders tractable decoders"
  Abstract: Compressed sensing is now established as an effective method for dimension reduction when the underlying signals are sparse or compressible with respect to some suitable basis or frame. One important, yet under-addressed problem regarding the compressive acquisition of analog signals is how to perform quantization. This is directly related to the important issues of how "compressed" compressed sensing is (in terms of the total number of bits one ends up using after acquiring the signal) and ultimately whether compressed sensing can be used to obtain compressed representations of suitable signals. Building on our recent work, we propose a concrete and practicable method for performing "analog-to-information conversion". Following a compressive signal acquisition stage, the proposed method consists of a quantization stage, based on $\Sigma\Delta$ (sigma-delta) quantization, and a subsequent encoding (compression) stage that fits within the framework of compressed sensing seamlessly. We prove that, using this method, we can convert analog compressive samples to compressed digital bitstreams and decode using tractable algorithms based on convex optimization. We prove that the proposed AIC provides a nearly optimal encoding of sparse and compressible signals. Finally, we present numerical experiments illustrating the effectiveness of the proposed analog-to-information converter.

Connected through hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "why are deep nets reversible a simple theory with implications for training"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

Connected through hyperedge HE-2 (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"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "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-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"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"

Connected through hyperedge HE-4 (induced by source paper "memoryless scalar quantization for random frames"):
  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"
    - "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 "quantized compressed sensing for partial random circulant matrices"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "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"
    - "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-6 (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"
    - "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"

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)