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 "provable dynamic robust pca or robust subspace tracking"):
  Abstract: Dynamic robust principal component analysis (PCA) refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lie in a low-dimensional subspace that can change with time, albeit slowly. The goal is to track this changing subspace over time in the presence of sparse outliers. We develop and study a novel algorithm, which we call simple-ReProCS, based on the recently introduced Recursive Projected Compressive Sensing (ReProCS) framework. This paper provides the first guarantee for dynamic RPCA that holds under weakened versions of standard RPCA assumptions, slow subspace change, and a lower bound assumption on most outlier magnitudes. Our result is significant because: 1) it removes the strong assumptions needed by the two previous complete guarantees for ReProCS-based algorithms; 2) it shows that it is possible to achieve significantly improved outlier tolerance, compared with all existing RPCA or dynamic RPCA solutions by exploiting the above-mentioned two simple extra assumptions; and 3) it proves that simple-ReProCS is online (after initialization), fast, and, has near-optimal memory complexity.

Includes members:
  Member "static and dynamic robust pca and matrix completion a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "list decodable subspace recovery via sum of squares")
  Member "robust subspace learning robust pca robust subspace tracking and robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "nearly optimal robust subspace tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "fast algorithms for robust pca via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "nearly optimal robust matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "online high rank matrix completion")
  Member "non convex robust pca":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust graph learning from noisy 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)