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: "exact recoverability from dense corrupted observations via l_1 minimization"
  Abstract: This paper confirms a surprising phenomenon first observed by Wright \textit{et al.} \cite{WYGSM_Face_2009_J} \cite{WM_denseError_2010_J} under different setting: given $m$ highly corrupted measurements $y = A_{\Omega \bullet} x^{\star} + e^{\star}$, where $A_{\Omega \bullet}$ is a submatrix whose rows are selected uniformly at random from rows of an orthogonal matrix $A$ and $e^{\star}$ is an unknown sparse error vector whose nonzero entries may be unbounded, we show that with high probability $\ell_1$-minimization can recover the sparse signal of interest $x^{\star}$ exactly from only $m = C \mu^2 k (\log n)^2$ where $k$ is the number of nonzero components of $x^{\star}$ and $\mu = n \max_{ij} A_{ij}^2$, even if nearly 100% of the measurements are corrupted. We further guarantee that stable recovery is possible when measurements are polluted by both gross sparse and small dense errors: $y = A_{\Omega \bullet} x^{\star} + e^{\star}+ \nu$ where $\nu$ is the small dense noise with bounded energy. Numerous simulation results under various settings are also presented to verify the validity of the theory as well as to illustrate the promising potential of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "high dimensional robust sparse regression"):
  with members:
    - "high dimensional robust m estimation arbitrary corruption and heavy tails"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing and matrix completion with constant proportion of corruptions"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "recovery of sparsely corrupted signals"
    - "a probabilistic and ripless theory of compressed sensing"
    - "robust pca via outlier pursuit"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-3 (induced by source paper "stable restoration and separation of approximately sparse signals"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "sparse signal separation in redundant dictionaries"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovery of sparsely corrupted signals"
    - "model based compressive sensing"
    - "new bounds for restricted isometry constants"

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)