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: "learning representations for neural network based classification using the information bottleneck principle"
  Abstract: In this theory paper, we investigate training deep neural networks (DNNs) for classification via minimizing the information bottleneck (IB) functional. We show that the resulting optimization problem suffers from two severe issues: First, for deterministic DNNs, either the IB functional is infinite for almost all values of network parameters, making the optimization problem ill-posed, or it is piecewise constant, hence not admitting gradient-based optimization methods. Second, the invariance of the IB functional under bijections prevents it from capturing properties of the learned representation that are desirable for classification, such as robustness and simplicity. We argue that these issues are partly resolved for stochastic DNNs, DNNs that include a (hard or soft) decision rule, or by replacing the IB functional with related, but more well-behaved cost functions. We conclude that recent successes reported about training DNNs using the IB framework must be attributed to such solutions. As a side effect, our results indicate limitations of the IB framework for the analysis of DNNs. We also note that rather than trying to repair the inherent problems in the IB functional, a better approach may be to design regularizers on latent representation enforcing the desired properties directly.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "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 conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"

Connected through hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "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"
    - "variational predictive information bottleneck"

Connected through hyperedge HE-3 (induced by source paper "bidet an efficient binarized object detector"):
  with members:
    - "thundernet towards real time generic object detection"
    - "embarrassingly simple binary representation learning"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "importance estimation for neural network pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "efficient neural network compression"
    - "private model compression via knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "minimal achievable sufficient statistic learning"):
  with members:
    - "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"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-5 (induced by source paper "feature selection of neural networks is skewed towards the less abstract cue"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "pareto optimal data compression for binary classification tasks"):
  with members:
    - "learnability for the information bottleneck"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-7 (induced by source paper "estimating information flow in neural networks"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "on the emergence of invariance and disentangling in deep representations"
    - "estimating mixture entropy with pairwise distances"
    - "noisy activation functions"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-8 (induced by source paper "nonlinear information bottleneck"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "fixing a broken elbo"
    - "opening the black box of deep neural networks via information"
    - "estimating mixture entropy with pairwise distances"
    - "deep variational information bottleneck"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep learning and the information bottleneck principle"
    - "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)