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: "the functional neural process"
  Abstract: We present a new family of exchangeable stochastic processes, the Functional Neural Processes (FNPs). FNPs model distributions over functions by learning a graph of dependencies on top of latent representations of the points in the given dataset. In doing so, they define a Bayesian model without explicitly positing a prior distribution over latent global parameters; they instead adopt priors over the relational structure of the given dataset, a task that is much simpler. We show how we can learn such models from data, demonstrate that they are scalable to large datasets through mini-batch optimization and describe how we can make predictions for new points via their posterior predictive distribution. We experimentally evaluate FNPs on the tasks of toy regression and image classification and show that, when compared to baselines that employ global latent parameters, they offer both competitive predictions as well as more robust uncertainty estimates.

Connected through hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"

Connected through hyperedge HE-2 (induced by source paper "the case for bayesian deep learning"):
  with members:
    - "output constrained bayesian neural networks"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "functional variational bayesian neural networks"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "understanding generalization through visualizations"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "classifier agnostic saliency map extraction"

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"
    - "conditional neural processes"
    - "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 "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "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-5 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "recurrent neural processes"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"
    - "a new multi vehicle trajectory generator to simulate vehicle to vehicle encounters"

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)