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 "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"):
  Abstract: In this paper we introduce the ice-start problem, i.e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative for the real-world machine learning applications. For instance, in the health-care domain, when training an AI system for predicting patient metrics from lab tests, obtaining every single measurement comes with a high cost. Active learning, where only the label is associated with a cost does not apply to such problem, because performing all possible lab tests to acquire a new training datum would be costly, as well as unnecessary due to redundancy. We propose Icebreaker, a principled framework to approach the ice-start problem. Icebreaker uses a full Bayesian Deep Latent Gaussian Model (BELGAM) with a novel inference method. Our proposed method combines recent advances in amortized inference and stochastic gradient MCMC to enable fast and accurate posterior inference. By utilizing BELGAM's ability to fully quantify model uncertainty, we also propose two information acquisition functions for imputation and active prediction problems. We demonstrate that BELGAM performs significantly better than the previous VAE (Variational autoencoder) based models, when the data set size is small, using both machine learning benchmarks and real-world recommender systems and health-care applications. Moreover, based on BELGAM, Icebreaker further improves the performance and demonstrate the ability to use minimum amount of the training data to obtain the highest test time performance.

Includes members:
  Member "interpretable outcome prediction with sparse bayesian neural networks in intensive care":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preserving patient privacy while training a predictive model of in hospital mortality")
  Member "active feature acquisition with supervised matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploratory machine learning with unknown unknowns")
  Member "handling incomplete heterogeneous data using vaes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder")
  Member "eddi efficient dynamic discovery of high value information with partial vae":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale educational question analysis with partial variational auto encoders")
  Member "generative adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "baod budget aware object detection")
  Member "advances in variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action for better prediction")
  Member "matrix completion with queries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on hybrid modular recommendation systems for video streaming")

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)