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: "neural processes"
  Abstract: A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Like GPs, NPs define distributions over functions, are capable of rapid adaptation to new observations, and can estimate the uncertainty in their predictions. Like NNs, NPs are computationally efficient during training and evaluation but also learn to adapt their priors to data. We demonstrate the performance of NPs on a range of learning tasks, including regression and optimisation, and compare and contrast with related models in the literature.

Connected through hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "guiding extractive summarization with question answering rewards"
    - "continual unsupervised representation learning"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "pegasus pre training with extracted gap sentences for abstractive summarization"
    - "putting machine translation in context with the noisy channel model"
    - "neural extractive text summarization with syntactic compression"
    - "text summarization with pretrained encoders"

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"
    - "attentive neural processes"
    - "deepmind control suite"
    - "conditional neural processes"
    - "invertible residual networks"
    - "neural ordinary differential equations"

Connected through hyperedge HE-3 (induced by source paper "model primitive hierarchical lifelong reinforcement learning"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "continual lifelong learning with neural networks a review"
    - "norml no reward meta learning"
    - "dac the double actor critic architecture for learning options"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "deepmind control suite"
    - "count based exploration with the successor representation"
    - "safe option critic learning safety in the option critic architecture"

Connected through hyperedge HE-4 (induced by source paper "texture fields learning texture representations in function space"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "learning category specific mesh reconstruction from image collections"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "im2avatar colorful 3d reconstruction from a single image"

Connected through hyperedge HE-5 (induced by source paper "meta reinforcement learning as task inference"):
  with members:
    - "guided meta policy search"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "emergent coordination through competition"
    - "deepmind control suite"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "neural predictive belief representations"
    - "learning dexterous in hand manipulation"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "novelty detection with gan"

Connected through hyperedge HE-7 (induced by source paper "uncertainty in model agnostic meta learning using variational inference"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "on calibration of modern neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "continual learning via online leverage score sampling"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "continual learning through synaptic intelligence"
    - "gradient episodic memory for continual learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)