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: "robustness guarantees for bayesian inference with gaussian processes"
  Abstract: Bayesian inference and Gaussian processes are widely used in applications ranging from robotics and control to biological systems. Many of these applications are safety-critical and require a characterization of the uncertainty associated with the learning model and formal guarantees on its predictions. In this paper we define a robustness measure for Bayesian inference against input perturbations, given by the probability that, for a test point and a compact set in the input space containing the test point, the prediction of the learning model will remain $\delta-$close for all the points in the set, for $\delta>0.$ Such measures can be used to provide formal guarantees for the absence of adversarial examples. By employing the theory of Gaussian processes, we derive tight upper bounds on the resulting robustness by utilising the Borell-TIS inequality, and propose algorithms for their computation. We evaluate our techniques on two examples, a GP regression problem and a fully-connected deep neural network, where we rely on weak convergence to GPs to study adversarial examples on the MNIST dataset.

Connected through hyperedge HE-1 (induced by source paper "robustness of bayesian neural networks to gradient based attacks"):
  with members:
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "uncertainty quantification with statistical guarantees in end to end autonomous driving control"
    - "theoretically principled trade off between robustness and accuracy"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "on the geometry of adversarial examples"
    - "gradient descent finds global minima of deep neural networks"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-2 (induced by source paper "adversarial vulnerability bounds for gaussian process classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "killing four birds with one gaussian process analyzing test time attack vectors on classification"
    - "the limitations of model uncertainty in adversarial settings"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "provably minimally distorted adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "statistical guarantees for the robustness of bayesian neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "verification of deep probabilistic models"
    - "the limitations of model uncertainty in adversarial settings"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "feature guided black box safety testing of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "uncertainty quantification with statistical guarantees in end to end autonomous driving control"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "central limit model checking"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-5 (induced by source paper "safety guarantees for planning based on iterative gaussian processes"):
  with members:
    - "uncertainty aware model based policy optimization"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "uncertainty aware reinforcement learning for collision avoidance"

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)