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: "meta learning priors for efficient online bayesian regression"
  Abstract: Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions. The predominant implementation of GP regression is a nonparameteric kernel-based approach, as it enables fitting of arbitrary nonlinear functions. However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive. In this work, we propose ALPaCA, an algorithm for efficient Bayesian regression which addresses these issues. ALPaCA uses a dataset of sample functions to learn a domain-specific, finite-dimensional feature encoding, as well as a prior over the associated weights, such that Bayesian linear regression in this feature space yields accurate online predictions of the posterior predictive density. These features are neural networks, which are trained via a meta-learning (or "learning-to-learn") approach. ALPaCA extracts all prior information directly from the dataset, rather than restricting prior information to the choice of kernel hyperparameters. Furthermore, by operating in the weight space, it substantially reduces sample complexity. We investigate the performance of ALPaCA on two simple regression problems, two simulated robotic systems, and on a lane-change driving task performed by humans. We find our approach outperforms kernel-based GP regression, as well as state of the art meta-learning approaches, thereby providing a promising plug-in tool for many regression tasks in robotics where scalability and data-efficiency are important.

Connected through hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-2 (induced by source paper "meta learning with implicit gradients"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "a differentiable augmented lagrangian method for bilevel nonlinear optimization"
    - "infinite mixture prototypes for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (induced by source paper "rapid learning or feature reuse towards understanding the effectiveness of maml"):
  with members:
    - "similarity of neural network representations revisited"
    - "meta learning with differentiable convex optimization"
    - "meta learning representations for continual learning"
    - "few shot text classification with distributional signatures"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "transfusion understanding transfer learning for medical imaging"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "continuous meta learning without tasks"):
  with members:
    - "meta learning representations for continual learning"
    - "introduction to online convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "a closer look at few shot classification"
    - "task free continual learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-5 (induced by source paper "meta learning without memorization"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "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"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-6 (induced by source paper "network offloading policies for cloud robotics a learning based approach"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "a lyapunov based approach to safe reinforcement learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "constrained policy optimization"
    - "deep bayesian active learning with image data"
    - "noscope optimizing neural network queries over video at scale"
    - "asynchronous methods for deep reinforcement learning"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "probabilistic safety constraints for learned high relative degree system dynamics"):
  with members:
    - "control barrier functions theory and applications"
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "self triggered control for safety critical systems using control barrier functions"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-8 (induced by source paper "barc backward reachability curriculum for robotic reinforcement learning"):
  with members:
    - "adapt zero shot adaptive policy transfer for stochastic dynamical systems"
    - "learning robotic assembly from cad"
    - "sim to real learning agile locomotion for quadruped robots"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "combining model based and model free updates for trajectory centric reinforcement 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.RO(Robotics)