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 "attentive neural processes"):
  Abstract: Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.

Includes members:
  Member "the variational homoencoder learning to learn high capacity generative models from few examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel bilevel paradigm for image to image translation")
  Member "image transformer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial code learning for image generation")
  Member "consistent generative query networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geometry aware neural rendering")
  Member "conditional neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local nonparametric meta learning")
  Member "vfunc a deep generative model for functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "marginalized state distribution entropy regularization in policy optimization")
  Member "learning models for visual 3d localization with implicit mapping":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometry aware neural rendering")
  Member "variational memory addressing in generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical quantized autoencoders")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)