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: "bayesian dark knowledge"
  Abstract: We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications involving bandits or active learning. One simple approach to this is to use online Monte Carlo methods, such as SGLD (stochastic gradient Langevin dynamics). Unfortunately, such a method needs to store many copies of the parameters (which wastes memory), and needs to make predictions using many versions of the model (which wastes time). #R##N#We describe a method for "distilling" a Monte Carlo approximation to the posterior predictive density into a more compact form, namely a single deep neural network. We compare to two very recent approaches to Bayesian neural networks, namely an approach based on expectation propagation [Hernandez-Lobato and Adams, 2015] and an approach based on variational Bayes [Blundell et al., 2015]. Our method performs better than both of these, is much simpler to implement, and uses less computation at test time.

Connected through hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "freelb enhanced adversarial training for language understanding"

Connected through hyperedge HE-2 (induced by source paper "scalable approximate inference and some applications"):
  with members:
    - "stein variational inference for discrete distributions"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "deep probabilistic video compression"
    - "a bandit approach to maximum inner product search"
    - "bnn improved binary network training"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "stein variational policy gradient"

Connected through hyperedge HE-3 (induced by source paper "sampling prediction matching examples in neural networks a probabilistic programming approach"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scenic a language for scenario specification and scene generation"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "pyro deep universal probabilistic programming"
    - "deep learning for classical japanese literature"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "self distillation amplifies regularization in hilbert space"):
  with members:
    - "hydra preserving ensemble diversity for model distillation"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation by on the fly native ensemble"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-5 (induced by source paper "assessing the adversarial robustness of monte carlo and distillation methods for deep bayesian neural network classification"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "densely connected convolutional networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-6 (induced by source paper "hydra preserving ensemble diversity for model distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "deep exploration via bootstrapped dqn"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-7 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "robustness properties of facebook s resnext wsl models"
    - "billion scale semi supervised learning for image classification"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "natural adversarial examples"
    - "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"

Connected through hyperedge HE-8 (induced by source paper "openei an open framework for edge intelligence"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "in datacenter performance analysis of a tensor processing unit"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"

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)