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: "corrupted sensing novel guarantees for separating structured signals"
  Abstract: We study the problem of corrupted sensing, a generalization of compressed sensing in which one aims to recover a signal from a collection of corrupted or unreliable measurements. While an arbitrary signal cannot be recovered in the face of arbitrary corruption, tractable recovery is possible when both signal and corruption are suitably structured. We quantify the relationship between signal recovery and two geometric measures of structure, the Gaussian complexity of a tangent cone, and the Gaussian distance to a subdifferential. We take a convex programming approach to disentangling signal and corruption, analyzing both penalized programs that tradeoff between signal and corruption complexity, and constrained programs that bound the complexity of signal or corruption when prior information is available. In each case, we provide conditions for exact signal recovery from structured corruption and stable signal recovery from structured corruption with added unstructured noise. Our simulations demonstrate close agreement between our theoretical recovery bounds and the sharp phase transitions observed in practice. In addition, we provide new interpretable bounds for the Gaussian complexity of sparse vectors, block-sparse vectors, and low-rank matrices, which lead to sharper guarantees of recovery when combined with our results and those in the literature.

Connected through hyperedge HE-1 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-2 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization"):
  with members:
    - "on the error in phase transition computations for compressed sensing"
    - "sample complexity of total variation minimization"
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "on the effective measure of dimension in the analysis cosparse model"

Connected through hyperedge HE-3 (induced by source paper "reconstruction under outliers for fourier sparse functions"):
  with members:
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "efficient algorithms for outlier robust regression"
    - "robust polynomial regression up to the information theoretic limit"
    - "fourier sparse interpolation without a frequency gap"
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "analysis of boolean functions"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"

Connected through hyperedge HE-4 (induced by source paper "recovery of structured signals from corrupted non linear measurements"):
  with members:
    - "corrupted sensing with sub gaussian measurements"
    - "stable recovery of structured signals from corrupted sub gaussian measurements"
    - "on the phase transition of corrupted sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "the generalized lasso with non linear observations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp recovery bounds for convex demixing with applications"

Connected through hyperedge HE-5 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "modulated unit norm tight frames for compressed sensing"
    - "convolutional compressed sensing using deterministic sequences"
    - "the restricted isometry property for random block diagonal matrices"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-6 (induced by source paper "on the error in phase transition computations for compressed sensing"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "simple bounds for recovering low complexity models"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "recovery of structured signals with prior information via maximizing correlation"):
  with members:
    - "stable recovery of structured signals from corrupted sub gaussian measurements"
    - "compressed sensing with prior information via maximizing correlation"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "estimating unknown sparsity in compressed sensing"
    - "optimal incorporation of sparsity information by weighted l 1 optimization"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "compressed sensing with adversarial sparse noise via l1 regression"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "robust regression via hard thresholding"
    - "compressed sensing and matrix completion with constant proportion of corruptions"

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)