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: "matrix completion with queries"
  Abstract: In many applications, e.g., recommender systems and traffic monitoring, the data comes in the form of a matrix that is only partially observed and low rank. A fundamental data-analysis task for these datasets is matrix completion, where the goal is to accurately infer the entries missing from the matrix. Even when the data satisfies the low-rank assumption, classical matrix-completion methods may output completions with significant error -- in that the reconstructed matrix differs significantly from the true underlying matrix. Often, this is due to the fact that the information contained in the observed entries is insufficient. In this work, we address this problem by proposing an active version of matrix completion, where queries can be made to the true underlying matrix. Subsequently, we design Order&Extend, which is the first algorithm to unify a matrix-completion approach and a querying strategy into a single algorithm. Order&Extend is able identify and alleviate insufficient information by judiciously querying a small number of additional entries. In an extensive experimental evaluation on real-world datasets, we demonstrate that our algorithm is efficient and is able to accurately reconstruct the true matrix while asking only a small number of queries.

Connected through hyperedge HE-1 (induced by source paper "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"):
  with members:
    - "interpretable outcome prediction with sparse bayesian neural networks in intensive care"
    - "active feature acquisition with supervised matrix completion"
    - "handling incomplete heterogeneous data using vaes"
    - "eddi efficient dynamic discovery of high value information with partial vae"
    - "generative adversarial active learning"
    - "advances in variational inference"
    - "deep bayesian active learning with image data"
    - "a utility theoretic approach to privacy in online services"

Connected through hyperedge HE-2 (induced by source paper "on hybrid modular recommendation systems for video streaming"):
  with members:
    - "neural autoregressive collaborative filtering for implicit feedback"
    - "hybrid recommender system based on autoencoders"
    - "feature weighted linear stacking"

Connected through hyperedge HE-3 (induced by source paper "active feature acquisition with supervised matrix completion"):
  with members:
    - "learning with feature evolvable streams"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "high rank matrix completion and subspace clustering with missing data"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"
    - "active learning with statistical models"

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.LG(Machine Learning)