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: "statistical guarantees for the robustness of bayesian neural networks"
  Abstract: We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two. Such a measure can be used, for instance, to quantify the probability of the existence of adversarial examples. Building on statistical verification techniques for probabilistic models, we develop a framework that allows us to estimate probabilistic robustness for a BNN with statistical guarantees, i.e., with a priori error and confidence bounds. We provide experimental comparison for several approximate BNN inference techniques on image classification tasks associated to MNIST and a two-class subset of the GTSRB dataset. Our results enable quantification of uncertainty of BNN predictions in adversarial settings.

Connected through hyperedge HE-1 (induced by source paper "robustness of bayesian neural networks to gradient based attacks"):
  with members:
    - "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"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-2 (induced by source paper "robustness of 3d deep learning in an adversarial setting"):
  with members:
    - "dynamic graph cnn for learning on point clouds"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "feature guided black box safety testing of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (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"
    - "central limit model checking"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "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"

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)