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: "nonlinear information bottleneck"
  Abstract: Information bottleneck [IB] is a technique for extracting information in some `input' random variable that is relevant for predicting some different 'output' random variable. IB works by encoding the input in a compressed 'bottleneck variable' from which the output can then be accurately decoded. IB can be difficult to compute in practice, and has been mainly developed for two limited cases: (1) discrete random variables with small state spaces, and (2) continuous random variables that are jointly Gaussian distributed (in which case the encoding and decoding maps are linear). We propose a method to perform IB in more general domains. Our approach can be applied to discrete or continuous inputs and outputs, and allows for nonlinear encoding and decoding maps. The method uses a novel upper bound on the IB objective, derived using a non-parametric estimator of mutual information and a variational approximation. We show how to implement the method using neural networks and gradient-based optimization, and demonstrate its performance on the MNIST dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"):
  with members:
    - "conditional generative models are not robust"
    - "hexagan generative adversarial nets for real world classification"
    - "sum of squares polynomial flow"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "hybrid models with deep and invertible features"
    - "guided image generation with conditional invertible neural networks"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-3 (induced by source paper "quantifying layerwise information discarding of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "stiffness a new perspective on generalization in neural networks"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "dynamic routing between capsules"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (induced by source paper "information bottleneck and its applications in deep learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "mathematics of deep learning"
    - "infovae information maximizing variational autoencoders"
    - "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"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-5 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "gradient target propagation"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-6 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (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"
    - "estimating information flow in neural networks"
    - "the variational deficiency bottleneck"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-8 (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"
    - "estimating information flow in neural networks"
    - "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"

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.IT(Information Theory)