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: "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.

Connected through hyperedge HE-1 (induced by source paper "local nonparametric meta learning"):
  with members:
    - "adaptive deep kernel learning"
    - "meta learning without memorization"
    - "towards understanding generalization in gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "fast context adaptation via meta learning"
    - "bayesian model agnostic meta learning"
    - "conditional neural processes"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "modeling continuous stochastic processes with dynamic normalizing flows"):
  with members:
    - "scalable gradients for stochastic differential equations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sequential neural processes"
    - "deepmind control suite"
    - "neural processes"
    - "conditional neural processes"
    - "invertible residual networks"
    - "neural ordinary differential equations"

Connected through hyperedge HE-3 (induced by source paper "pi vae encoding stochastic process priors with variational autoencoders"):
  with members:
    - "an introduction to variational autoencoders"
    - "exact gaussian processes on a million data points"
    - "quality of uncertainty quantification for bayesian neural network inference"
    - "hamiltonian variational auto encoder"
    - "conditional neural processes"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "adam a method for stochastic optimization"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-4 (induced by source paper "graph element networks adaptive structured computation and memory"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "a comprehensive survey on graph neural networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "graph neural networks a review of methods and applications"
    - "modular meta learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-5 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"):
  with members:
    - "meta reinforcement learning as task inference"
    - "conditional neural processes"
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "the perception distortion tradeoff"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"
    - "the statistical recurrent unit"

Connected through hyperedge HE-7 (induced by source paper "neural approximation of an auto regressive process through confidence guided sampling"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "mask predict parallel decoding of conditional masked language models"
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "non monotonic sequential text generation"
    - "conditional neural processes"
    - "blockwise parallel decoding for deep autoregressive models"
    - "fast generation for convolutional autoregressive models"
    - "neural speed reading via skim rnn"

Connected through hyperedge HE-8 (induced by source paper "the functional neural process"):
  with members:
    - "functional variational bayesian neural networks"
    - "correlated variational auto encoders"
    - "eigenvalue corrected noisy natural gradient"
    - "associative compression networks for representation learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "noisy natural gradient as variational inference"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

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)