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: "conditional neural processes"
  Abstract: Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.

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"
    - "attentive neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "fast context adaptation via meta learning"
    - "bayesian model agnostic meta learning"
    - "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"
    - "attentive neural processes"
    - "deepmind control suite"
    - "neural processes"
    - "invertible residual networks"
    - "neural ordinary differential equations"

Connected through hyperedge HE-3 (induced by source paper "energy based processes for exchangeable data"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "neural similarity learning"
    - "the functional neural process"
    - "pointgrow autoregressively learned point cloud generation with self attention"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "vconv dae deep volumetric shape learning without object labels"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-4 (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"
    - "attentive neural processes"
    - "hamiltonian variational auto encoder"
    - "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-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"
    - "neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "neural predictive belief representations"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-6 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-7 (induced by source paper "modular meta learning with shrinkage"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "optimizing millions of hyperparameters by implicit differentiation"
    - "meta learning with differentiable convex optimization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-8 (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"
    - "attentive 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"

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)