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 "weighted matrix completion and recovery with prior subspace information"):
  Abstract: An incoherent low-rank matrix can be efficiently reconstructed after observing a few of its entries at random, and then solving a convex program that minimizes the nuclear norm. In many applications, in addition to these entries, potentially valuable prior knowledge about the column and row spaces of the matrix is also available to the practitioner. In this paper, we incorporate this prior knowledge in matrix completion---by minimizing a weighted nuclear norm---and precisely quantify any improvements. In particular, we find in theory that reliable prior knowledge reduces the sample complexity of matrix completion by a logarithmic factor, and the observed improvement in numerical simulations is considerably more magnified. We also present similar results for the closely related problem of matrix recovery from generic linear measurements.

Includes members:
  Member "what to expect when you are expecting on the grassmannian":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction")
  Member "streaming principal component analysis from incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling")
  Member "mc 2 a two phase algorithm for leveraged matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "incoherence optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "provable inductive matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "weighted ell_1 minimization with multiple weighting sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "recovering compressively sampled signals using partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting prior information in block sparse signals")

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)