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: "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.

Connected through hyperedge HE-1 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "robust frequent directions with application in online learning"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"

Connected through hyperedge HE-2 (induced by source paper "weighted matrix completion and recovery with prior subspace information"):
  with members:
    - "streaming principal component analysis from incomplete data"
    - "mc 2 a two phase algorithm for leveraged matrix completion"
    - "incoherence optimal matrix completion"
    - "provable inductive matrix completion"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "recovering compressively sampled signals using partial support information"
    - "a simpler approach to matrix completion"

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)