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: "the conditional entropy bottleneck"
  Abstract: Much of the field of Machine Learning exhibits a prominent set of failure modes, including vulnerability to adversarial examples, poor out-of-distribution (OoD) detection, miscalibration, and willingness to memorize random labelings of datasets. We characterize these as failures of robust generalization, which extends the traditional measure of generalization as accuracy or related metrics on a held-out set. We hypothesize that these failures to robustly generalize are due to the learning systems retaining too much information about the training data. To test this hypothesis, we propose the Minimum Necessary Information (MNI) criterion for evaluating the quality of a model. In order to train models that perform well with respect to the MNI criterion, we present a new objective function, the Conditional Entropy Bottleneck (CEB), which is closely related to the Information Bottleneck (IB). We experimentally test our hypothesis by comparing the performance of CEB models with deterministic models and Variational Information Bottleneck (VIB) models on a variety of different datasets and robustness challenges. We find strong empirical evidence supporting our hypothesis that MNI models improve on these problems of robust generalization.

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"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "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"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "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 "dueling decoders regularizing variational autoencoder latent spaces"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning latent dynamics for planning from pixels"
    - "therml the thermodynamics of machine learning"
    - "group normalization"
    - "uncertainty in the variational information bottleneck"
    - "cross modal deep variational hand pose estimation"
    - "β vaes can retain label information even at high compression"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-4 (induced by source paper "entropy minimization in emergent languages"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "on the pitfalls of measuring emergent communication"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "compositional obverter communication learning from raw visual input"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-5 (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"
    - "learnability for the information 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-6 (induced by source paper "information minimization in emergent languages"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-7 (induced by source paper "information bottleneck approach to salient region discovery"):
  with members:
    - "infomask masked variational latent representation to localize chest disease"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "self erasing network for integral object attention"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "deep variational information bottleneck"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-8 (induced by source paper "learnability for the information bottleneck"):
  with members:
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "strong data processing inequalities for channels and bayesian networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep variational information bottleneck"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)