human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "low rank matrix recovery from errors and erasures"):
  Abstract: This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when the natural convex relaxation of minimizing rank plus support succeeds in exact recovery. Our result allows for the simultaneous presence of random and deterministic components in both the error and erasure patterns. On the one hand, corollaries obtained by specializing this one single result in different ways recover (up to poly-log factors) all the existing works in matrix completion, and sparse and low-rank matrix recovery. On the other hand, our results also provide the first guarantees for (a) recovery when we observe a vanishing fraction of entries of a corrupted matrix, and (b) deterministic matrix completion.

Includes members:
  Member "compressed sensing and matrix completion with constant proportion of corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "clustering partially observed graphs via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "dense error correction for low rank matrices via principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "nuclear norm minimization for the planted clique and biclique problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "a simpler approach to matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "federated over the air subspace learning from incomplete data")

Question: Please tell me which class the source paper 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)