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: "estimating information flow in neural networks"
  Abstract: We study the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information $I(X;T)$ between the input $X$ and internal representations $T$ decreases. Several papers observe compression of estimated mutual information on different DNN models, but the true $I(X;T)$ over these networks is provably either constant (discrete $X$) or infinite (continuous $X$). This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works. To this end, we introduce an auxiliary (noisy) DNN framework for which $I(X;T)$ is a meaningful quantity that depends on the network's parameters. This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations. We then develop a rigorous estimator for $I(X;T)$ in noisy DNNs and observe compression in various models. By relating $I(X;T)$ in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class. Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the $T$ space. Finally, we return to the estimator of $I(X;T)$ employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering. This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

Connected through hyperedge HE-1 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "mutual information scaling and expressive power of sequence models"):
  with members:
    - "sharp nearby fuzzy far away how neural language models use context"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "do neural nets learn statistical laws behind natural language"
    - "opening the black box of deep neural networks via information"
    - "sgdr stochastic gradient descent with restarts"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-3 (induced by source paper "provably scale covariant hierarchical continuous networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "mathematics of deep learning"
    - "dynamic routing between capsules"
    - "synthesizing robust adversarial examples"
    - "a spatiotemporal oriented energy network for dynamic texture recognition"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-4 (induced by source paper "learning representations for neural network based classification using the information bottleneck principle"):
  with members:
    - "understanding individual neuron importance using information theory"
    - "uncertainty in the variational information bottleneck"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "i revnet deep invertible networks"
    - "the variational deficiency bottleneck"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "neural networks regularization through class wise invariant representation learning"

Connected through hyperedge HE-5 (induced by source paper "minimal achievable sufficient statistic learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "uncertainty in the variational information bottleneck"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "emergence of invariance and disentanglement in deep representations"
    - "gradient regularization improves accuracy of discriminative models"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "opening the black box of deep neural networks via information"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-6 (induced by source paper "where is the information in a deep neural network"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "the information complexity of learning tasks their structure and their distance"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "train faster generalize better stability of stochastic gradient descent"
    - "new insights and perspectives on the natural gradient method"
    - "a pac bayesian tutorial with a dropout bound"

Connected through hyperedge HE-7 (induced by source paper "the role of a layer in deep neural networks a gaussian process perspective"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "opening the black box of deep neural networks via information"
    - "estimating mixture entropy with pairwise distances"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning and the information bottleneck principle"
    - "difference target propagation"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "understanding convolutional neural networks with information theory an initial exploration"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "understanding individual neuron importance using information theory"
    - "understanding autoencoders with information theoretic concepts"
    - "simple stopping criteria for information theoretic feature selection"
    - "emergence of invariance and disentanglement in deep representations"
    - "thinet a filter level pruning method for deep neural network compression"
    - "opening the black box of deep neural networks via information"
    - "the hasyv2 dataset"

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)