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: "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.

Connected through hyperedge HE-1 (induced by source paper "exploratory machine learning with unknown unknowns"):
  with members:
    - "an unbiased risk estimator for learning with augmented classes"
    - "fast classification rates without standard margin assumptions"
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"

Connected through hyperedge HE-2 (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"
    - "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"
    - "matrix completion with queries"
    - "a utility theoretic approach to privacy in online services"

Connected through hyperedge HE-3 (induced by source paper "alipy active learning in python"):
  with members:
    - "learning active learning from data"

Connected through hyperedge HE-4 (induced by source paper "eddi efficient dynamic discovery of high value information with partial vae"):
  with members:
    - "handling incomplete heterogeneous data using vaes"
    - "conditional neural processes"
    - "deep sets"
    - "advances in variational inference"
    - "adam a method for stochastic optimization"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"

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)