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: "opening the black box of deep neural networks via information"
  Abstract: Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work proposed to analyze DNNs in the \textit{Information Plane}; i.e., the plane of the Mutual Information values that each layer preserves on the input and output variables. They suggested that the goal of the network is to optimize the Information Bottleneck (IB) tradeoff between compression and prediction, successively, for each layer. #R##N#In this work we follow up on this idea and demonstrate the effectiveness of the Information-Plane visualization of DNNs. Our main results are: (i) most of the training epochs in standard DL are spent on {\emph compression} of the input to efficient representation and not on fitting the training labels. (ii) The representation compression phase begins when the training errors becomes small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift to smaller training error into a stochastic relaxation, or random diffusion, constrained by the training error value. (iii) The converged layers lie on or very close to the Information Bottleneck (IB) theoretical bound, and the maps from the input to any hidden layer and from this hidden layer to the output satisfy the IB self-consistent equations. This generalization through noise mechanism is unique to Deep Neural Networks and absent in one layer networks. (iv) The training time is dramatically reduced when adding more hidden layers. Thus the main advantage of the hidden layers is computational. This can be explained by the reduced relaxation time, as this it scales super-linearly (exponentially for simple diffusion) with the information compression from the previous layer.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

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 "explaining knowledge distillation by quantifying the knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "transferring knowledge across learning processes"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "i revnet deep invertible networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "is the meta learning idea able to improve the generalization of deep neural networks on the standard supervised learning"):
  with members:
    - "alpha maml adaptive model agnostic meta learning"
    - "on first order meta learning algorithms"
    - "training behavior of deep neural network in frequency domain"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "how to train your maml"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "auto meta automated gradient based meta learner search"

Connected through hyperedge HE-6 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-7 (induced by source paper "a free energy principle for representation learning"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "therml the thermodynamics of machine learning"
    - "taskonomy disentangling task transfer learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "fixing a broken elbo"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-8 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"

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)