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 "active feature acquisition with supervised matrix completion"):
  Abstract: Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature acquisition usually involves special devices or complex process, it is expensive to acquire all feature values for the whole dataset. On the other hand, features may be correlated with each other, and some values may be recovered from the others. It is thus important to decide which features are most informative for recovering the other features as well as improving the learning performance. In this paper, we try to train an effective classification model with least acquisition cost by jointly performing active feature querying and supervised matrix completion. When completing the feature matrix, a novel target function is proposed to simultaneously minimize the reconstruction error on observed entries and the supervised loss on training data. When querying the feature value, the most uncertain entry is actively selected based on the variance of previous iterations. In addition, a bi-objective optimization method is presented for cost-aware active selection when features bear different acquisition costs. The effectiveness of the proposed approach is well validated by both theoretical analysis and experimental study.

Includes members:
  Member "learning with feature evolvable streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dual averaging method for online graph structured sparsity")
  Member "matrix completion with queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "icebreaker element wise active information acquisition with bayesian deep latent gaussian model")
  Member "the algebraic combinatorial approach for low rank 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 "high rank matrix completion and subspace clustering with missing data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "uniqueness of low rank matrix completion by rigidity theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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.LG(Machine Learning)