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: "learnability for the information bottleneck"
  Abstract: The Information Bottleneck (IB) method provides an insightful and principled approach for balancing compression and prediction for representation learning. The IB objective     I ( X ; Z ) − β I ( Y ; Z )     employs a Lagrange multiplier    β    to tune this trade-off. However, in practice, not only is    β    chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between    β   , learnability, the intrinsic nature of the dataset and model capacity. In this paper, we show that if    β    is improperly chosen, learning cannot happen—the trivial representation     P ( Z | X ) = P ( Z )     becomes the global minimum of the IB objective. We show how this can be avoided, by identifying a sharp phase transition between the unlearnable and the learnable which arises as    β    is varied. This phase transition defines the concept of IB-Learnability. We prove several sufficient conditions for IB-Learnability, which provides theoretical guidance for choosing a good    β   . We further show that IB-learnability is determined by the largest confident, typical and imbalanced subset of the examples (the conspicuous subset), and discuss its relation with model capacity. We give practical algorithms to estimate the minimum    β    for a given dataset. We also empirically demonstrate our theoretical conditions with analyses of synthetic datasets, MNIST and CIFAR10.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"

Connected through hyperedge HE-2 (induced by source paper "phase transitions for the information bottleneck in representation learning"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "pareto optimal data compression for binary classification tasks"
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep variational information bottleneck"

Connected through hyperedge HE-3 (induced by source paper "ceb improves model robustness"):
  with members:
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a fourier perspective on model robustness in computer vision"
    - "the conditional entropy bottleneck"
    - "feature denoising for improving adversarial robustness"
    - "autoaugment learning augmentation policies from data"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "do compressed representations generalize better"):
  with members:
    - "improving generalization of deep networks for inverse reconstruction of image sequences"
    - "information bottleneck methods for distributed learning"
    - "layer wise learning of stochastic neural networks with information bottleneck"
    - "opening the black box of deep neural networks via information"
    - "nonlinear information bottleneck"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "flow of information in feed forward deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "pareto optimal data compression for binary classification tasks"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-6 (induced by source paper "the conditional entropy bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scaling provable adversarial defenses"
    - "uncertainty in the variational information bottleneck"
    - "implicit reparameterization gradients"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "autoaugment learning augmentation policies from data"
    - "representation learning with contrastive predictive coding"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)