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: "cyclical stochastic gradient mcmc for bayesian deep learning"
  Abstract: The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We prove that our proposed learning rate schedule provides faster convergence to samples from a stationary distribution than SG-MCMC with standard decaying schedules. Moreover, we provide extensive experimental results to demonstrate the effectiveness of cyclical SG-MCMC in learning complex multimodal distributions, especially for fully Bayesian inference with modern deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world"):
  with members:
    - "towards a human like open domain chatbot"
    - "mass masked sequence to sequence pre training for language generation"
    - "sequence embeddings help to identify fraudulent cases in healthcare insurance"
    - "soft edit distance for differentiable comparison of symbolic sequences"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"

Connected through hyperedge HE-2 (induced by source paper "the case for bayesian deep learning"):
  with members:
    - "output constrained bayesian neural networks"
    - "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"
    - "the functional neural process"
    - "classifier agnostic saliency map extraction"

Connected through hyperedge HE-3 (induced by source paper "amagold amortized metropolis adjustment for efficient stochastic gradient mcmc"):
  with members:
    - "minibatch gibbs sampling on large graphical models"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "scalable bayesian learning of recurrent neural networks for language modeling"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-4 (induced by source paper "evaluating scalable bayesian deep learning methods for robust computer vision"):
  with members:
    - "evaluating model calibration in classification"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "lightweight probabilistic deep networks"
    - "ocnet object context network for scene parsing"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pyro deep universal probabilistic programming"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-5 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning"):
  with members:
    - "swalp stochastic weight averaging in low precision training"
    - "subspace inference for bayesian deep learning"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-6 (induced by source paper "cyclical annealing schedule a simple approach to mitigating kl vanishing"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "latent intention dialogue models"
    - "snapshot ensembles train 1 get m for free"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "implicit posterior variational inference for deep gaussian processes"):
  with members:
    - "collective online learning of gaussian processes in massive multi agent systems"
    - "adversarial distillation of bayesian neural network posteriors"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational inference for bayesian sparse gaussian process regression"
    - "wavenet a generative model for raw audio"
    - "tensorflow a system for large scale machine learning"
    - "variational auto encoded deep gaussian processes"

Connected through hyperedge HE-8 (induced by source paper "deepflow history matching in the space of deep generative models"):
  with members:
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "adversarial regularizers in inverse problems"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "improved training of wasserstein gans"
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"

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)