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: "near optimal encoding for sigma delta quantization of finite frame expansions"
  Abstract: In this paper we investigate encoding the bit-stream resulting from coarse Sigma-Delta quantization of finite frame expansions (i.e., overdetermined representations) of vectors. We show that for a wide range of finite-frames, including random frames and piecewise smooth frames, there exists a simple encoding algorithm ---acting only on the Sigma-Delta bit stream--- and an associated decoding algorithm that together yield an approximation error which decays exponentially in the number of bits used. The encoding strategy consists of applying a discrete random operator to the Sigma-Delta bit stream and assigning a binary codeword to the result. The reconstruction procedure is essentially linear and equivalent to solving a least squares minimization problem.

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"
    - "noise shaping quantization methods for frame based and compressive sampling systems"

Connected through hyperedge HE-2 (induced by source paper "quantization for low rank matrix recovery"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "quantization and compressive sensing"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "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-3 (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"
    - "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-4 (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"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"

Connected through hyperedge HE-5 (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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "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-6 (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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "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:
    - "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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "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:
    - "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"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "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)