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 "what to expect when you are expecting on the grassmannian"):
  Abstract: Consider an incoming sequence of vectors, all belonging to an unknown subspace    $\text{S}$  , and each with many missing entries. In order to estimate   $\text{S}$  , it is common to partition the data into blocks and iteratively update the estimate of   $\text{S}$    with each new incoming measurement block. In this letter, we investigate a rather basic question: Is it possible to identify   $\text{S}$   by averaging the range of the partially observed incoming measurement blocks on the Grassmannian? We show that, in general, the span of the incoming blocks is in fact a biased estimator of    $\text{S}$   when data suffer from erasures, and we find an upper bound for this bias. We reach this conclusion by examining the defining optimization program for the  Frechet expectation   on the Grassmannian, and with the aid of a sharp perturbation bound and standard large deviation results.

Includes members:
  Member "intrinsic grassmann averages for online linear robust and nonlinear subspace learning":
    also connected through other hyperedges:
      (none)
  Member "streaming principal component analysis from incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling")
  Member "snipe for memory limited pca from incomplete data":
    also connected through other hyperedges:
      (none)
  Member "incoherence optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "local convergence of an algorithm for subspace identification from partial data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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)