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: "where is the information in a deep neural network"
  Abstract: Whatever information a Deep Neural Network has gleaned from past data is encoded in its weights. How this information affects the response of the network to future data is largely an open question. In fact, even how to define and measure information in a network is still not settled. We introduce the notion of Information in the Weights as the optimal trade-off between accuracy of the network and complexity of the weights, relative to a prior. Depending on the prior, the definition reduces to known information measures such as Shannon Mutual Information and Fisher Information, but affords added flexibility that enables us to relate it to generalization, via the PAC-Bayes bound, and to invariance. This relation hinges not only on the architecture of the model, but surprisingly on how it is trained. We then introduce a notion of effective information in the activations, which are deterministic functions of future inputs, resolving inconsistencies in prior work. We relate this to the Information in the Weights, and use this result to show that models of low complexity not only generalize better, but are bound to learn invariant representations of future inputs.

Connected through hyperedge HE-1 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "deep self learning from noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "unsupervised label noise modeling and loss correction"
    - "meta learning without memorization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "measuring the intrinsic dimension of objective landscapes"

Connected through hyperedge HE-2 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "thieves on sesame street model extraction of bert based apis"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "certified data removal from machine learning models"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "machine unlearning"

Connected through hyperedge HE-3 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "asymmetric valleys beyond sharp and flat local minima"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "don t decay the learning rate increase the batch size"
    - "sharp minima can generalize for deep nets"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "extracting robust and accurate features via a robust information bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "unifying the brascamp lieb inequality and the entropy power inequality"
    - "a family of bayesian cram er rao bounds and consequences for log concave priors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "does learning require memorization a short tale about a long tail"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "a spectral view of adversarially robust features"

Connected through hyperedge HE-6 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "beauty and the beast optimal methods meet learning for drone racing"

Connected through hyperedge HE-7 (induced by source paper "eternal sunshine of the spotless net selective forgetting in deep networks"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "knock knock who s there membership inference on aggregate location data"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "logan membership inference attacks against generative models"
    - "machine learning models that remember too much"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "information in infinite ensembles of infinitely wide neural networks"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "on variational bounds of mutual information"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "learners that use little information"
    - "opening the black box of deep neural networks via information"
    - "deep variational information bottleneck"
    - "deep learning and the information bottleneck principle"

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)