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: "deep learning and the information bottleneck principle"
  Abstract: Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "diversity in faces"
    - "toward gender inclusive coreference resolution"
    - "robust ordinal vae employing noisy pairwise comparisons for disentanglement"
    - "weakly supervised disentanglement with guarantees"
    - "contrastive variational autoencoder enhances salient features"
    - "weakly supervised disentanglement by pairwise similarities"

Connected through hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "softtriple loss deep metric learning without triplet sampling"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-3 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "bag of freebies for training object detection neural networks"
    - "learning to generate synthetic data via compositing"
    - "on physical adversarial patches for object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-4 (induced by source paper "understanding and enhancing mixed sample data augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "natural adversarial examples"
    - "fast autoaugment"
    - "data augmentation revisited rethinking the distribution gap between clean and augmented data"
    - "human uncertainty makes classification more robust"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-5 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning efficient representation for intrinsic motivation"
    - "on variational bounds of mutual information"
    - "learning representations in reinforcement learning an information bottleneck approach"
    - "unsupervised visuomotor control through distributional planning networks"
    - "the limits and potentials of deep learning for robotics"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "particle filter networks with application to visual localization"

Connected through hyperedge HE-6 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-7 (induced by source paper "think global act local relating dnn generalisation and node level snr"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "tightening mutual information based bounds on generalization error"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "on variational bounds of mutual information"
    - "entropy and mutual information in models of deep neural networks"
    - "putting an end to end to end gradient isolated learning of representations"
    - "chaining mutual information and tightening generalization bounds"
    - "theory iiib generalization in deep networks"

Connected through hyperedge HE-8 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"

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)