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 "moses a streaming algorithm for linear dimensionality reduction"):
  Abstract: This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors are presented sequentially to a user who has limited storage and processing time available. Applied to such problems, MOSES can provide a running estimate of leading principal components of the data that has arrived so far and also reduce its dimension. #R##N#MOSES generalises the popular incremental Singular Vale Decomposition (iSVD) to handle thin blocks of data, rather than just vectors. This minor generalisation in part allows us to complement MOSES with a comprehensive statistical analysis, thus providing the first theoretically-sound variant of iSVD, which has been lacking despite the empirical success of this method. This generalisation also enables us to concretely interpret MOSES as an approximate solver for the underlying non-convex optimisation program. We find that MOSES consistently surpasses the state of the art in our numerical experiments with both synthetic and real-world datasets, while being computationally inexpensive.

Includes members:
  Member "robust frequent directions with application in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "what to expect when you are expecting on the grassmannian":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted matrix completion and recovery with prior subspace information")
  Member "fixed rank approximation of a positive semidefinite matrix from streaming data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable kernel k means clustering with nystrom approximation relative error bounds")
  Member "streaming principal component analysis from incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling")
  Member "weighted matrix completion and recovery with prior subspace information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "an overview of low rank matrix recovery from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "learning the differential correlation matrix of a smooth function from point samples":
    also connected through other hyperedges:
      (none)

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)