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 "pca in data dependent noise correlated pca nearly optimal finite sample guarantees"):
  Abstract: We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, hence, the noise and the true data are correlated. Under a bounded-ness assumption on both the true data and noise, and a few assumptions on the data-noise correlation, we obtain a sample complexity bound for the most common PCA solution, singular value decomposition (SVD). This bound, which is within a logarithmic factor of the best achievable, significantly improves upon our bound from recent work (NIPS 2016) where we first studied this "correlated-PCA" problem.

Includes members:
  Member "fast algorithms for robust pca via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")
  Member "correlated pca principal components analysis when data and noise are correlated":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "nearly optimal robust matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "online matrix completion and online robust pca":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online high rank matrix completion")
  Member "non convex robust pca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "recursive robust pca or recursive sparse recovery in large but structured noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)