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: "dropout inference in bayesian neural networks with alpha divergences"
  Abstract: To obtain uncertainty estimates with real-world Bayesian deep learning models, practical inference approximations are needed. Dropout variational inference (VI) for example has been used for machine vision and medical applications, but VI can severely underestimates model uncertainty. Alpha-divergences are alternative divergences to VI's KL objective, which are able to avoid VI's uncertainty underestimation. But these are hard to use in practice: existing techniques can only use Gaussian approximating distributions, and require existing models to be changed radically, thus are of limited use for practitioners. We propose a re-parametrisation of the alpha-divergence objectives, deriving a simple inference technique which, together with dropout, can be easily implemented with existing models by simply changing the loss of the model. We demonstrate improved uncertainty estimates and accuracy compared to VI in dropout networks. We study our model's epistemic uncertainty far away from the data using adversarial images, showing that these can be distinguished from non-adversarial images by examining our model's uncertainty.

Connected through hyperedge HE-1 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "cross entropy loss and low rank features have responsibility for adversarial examples"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep skinny neural networks are not universal approximators"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-2 (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"
    - "robustness guarantees for bayesian inference with gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  with members:
    - "pose estimator and tracker using temporal flow maps for limbs"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "a fusion approach for multi frame optical flow estimation"

Connected through hyperedge HE-4 (induced by source paper "survey of dropout methods for deep neural networks"):
  with members:
    - "effective and efficient dropout for deep convolutional neural networks"
    - "adversarial dropout for recurrent neural networks"
    - "ising dropout a regularization method for training and compression of deep neural networks"
    - "recent advances in recurrent neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "improved regularization of convolutional neural networks with cutout"
    - "adversarial dropout regularization"

Connected through hyperedge HE-5 (induced by source paper "a robust approach for securing audio classification against adversarial attacks"):
  with members:
    - "bottom up broadcast neural network for music genre classification"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "improving transferability of adversarial examples with input diversity"
    - "audio adversarial examples targeted attacks on speech to text"
    - "inaudible voice commands"

Connected through hyperedge HE-6 (induced by source paper "model predictive policy learning with uncertainty regularization for driving in dense traffic"):
  with members:
    - "stochastic video generation with a learned prior"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "stochastic variational video prediction"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "imagination augmented agents for deep reinforcement learning"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-7 (induced by source paper "variational inference mpc for bayesian model based reinforcement learning"):
  with members:
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "an online learning approach to model predictive control"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "mirror descent search and its acceleration"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-8 (induced by source paper "unsupervised synthesis of anomalies in videos transforming the normal"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning features by watching objects move"
    - "learning temporal regularity in video sequences"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"

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)