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: "eddi efficient dynamic discovery of high value information with partial vae"
  Abstract: Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI, we propose a novel partial variational autoencoder (Partial VAE) to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables. We show cost reduction at the same decision quality and improved decision quality at the same cost in multiple machine learning benchmarks and two real-world health-care applications.

Connected through hyperedge HE-1 (induced by source paper "large scale educational question analysis with partial variational auto encoders"):
  with members:
    - "deep sets"
    - "advances in variational inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "dadi dynamic discovery of fair information with adversarial reinforcement learning"):
  with members:
    - "preserving differential privacy in adversarial learning with provable robustness"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "active fairness in algorithmic decision making"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on loss functions for deep neural networks in classification"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "interpretable outcome prediction with sparse bayesian neural networks in intensive care"):
  with members:
    - "advances in variational inference"
    - "end to end learning for self driving cars"
    - "gram graph based attention model for healthcare representation learning"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "natural language processing almost from scratch"

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)