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 "multi source deep gaussian process kernel learning"):
  Abstract: For many problems, relevant data are plentiful but explicit knowledge is not. Predictions about target variables may be informed by data sources that are noisy but plentiful, or data which the target variable is merely some function of. Intrepretable and flexible machine learning methods capable of fusing data across sources are lacking. We generalize the Deep Gaussian Processes so that GPs in intermediate layers can represent the posterior distribution summarizing the data from a related source. We model the prior-posterior stacking DGP with a single GP. The exact second moment of DGP is calculated analytically, and is taken as the kernel function for GP. The result is a kernel that captures effective correlation through function composition, reflects the structure of the observations from other data sources, and can be used to inform prediction based on limited direct observations. Therefore, the approximation of prior-posterior DGP can be considered a novel kernel composition which blends the kernels in different layers and have explicit dependence on the data. We consider two synthetic multi-source prediction problems: a) predicting a target variable that is merely a function of the source data and b) predicting noise-free data using a kernel trained on noisy data. Our method produces better prediction and tighter uncertainty on the synthetic data when comparing with standard GP and other DGP method, suggesting that our data-informed approximate DGPs are a powerful tool for integrating data across sources.

Includes members:
  Member "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "interpretable deep gaussian processes with moments":
    also connected through other hyperedges:
      (none)
  Member "neural tangent kernel convergence and generalization in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "deep multi fidelity gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data")
  Member "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "deep kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "generalized denoising auto encoders as generative models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "masking schemes for universal marginalisers")
  Member "gaussian processes for big data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")

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)