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: "improved bounds for sparse recovery from subsampled random convolutions"
  Abstract: We study the recovery of sparse vectors from subsampled random convolutions via $\ell_1$-minimization. We consider the setup in which both the subsampling locations as well as the generating vector are chosen at random. For a subgaussian generator with independent entries, we improve previously known estimates: if the sparsity $s$ is small enough, i.e., $s \lesssim \sqrt{n/\log(n)}$, we show that $m \gtrsim s \log(en/s)$ measurements are sufficient to recover $s$-sparse vectors in dimension $n$ with high probability, matching the well-known condition for recovery from standard Gaussian measurements. If $s$ is larger, then essentially $m \geq s \log^2(s) \log(\log(s)) \log(n)$ measurements are sufficient, again improving over previous estimates. Our results are shown via the so-called robust null space property which is weaker than the standard restricted isometry property. Our method of proof involves a novel combination of small ball estimates with chaining techniques {which should be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond"):
  with members:
    - "structured linear contextual bandits a sharp and geometric smoothed analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "leveraged volume sampling for linear regression"
    - "subsampling for ridge regression via regularized volume sampling"
    - "structured compressed sensing from theory to applications"
    - "randomized algorithms for matrices and data"
    - "a sparse johnson lindenstrauss transform"

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"
    - "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"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"

Connected through hyperedge HE-3 (induced by source paper "sparse power factorization balancing peakiness and sample complexity"):
  with members:
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "solving equations of random convex functions via anchored regression"
    - "blind demixing and deconvolution at near optimal rate"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "robust one bit compressed sensing with partial circulant matrices"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-5 (induced by source paper "improved bounds for the rip of subsampled circulant matrices"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "one bit compressed sensing with partial gaussian circulant matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "on the gap between rip properties and sparse recovery conditions"
    - "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"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"

Connected through hyperedge HE-7 (induced by source paper "quantized compressed sensing for partial random circulant matrices"):
  with members:
    - "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"
    - "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-8 (induced by source paper "preserving injectivity under subgaussian mappings and its application to compressed sensing"):
  with members:
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "the gap between the null space property and the restricted isometry property"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "a null space analysis of the l1 synthesis method in dictionary based compressed sensing"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "performance analysis of ell_1 synthesis with coherent frames"
    - "sparse recovery with orthogonal matching pursuit under rip"

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)