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: "deep double descent where bigger models and more data hurt"
  Abstract: We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.

Connected through hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-2 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-3 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited"):
  with members:
    - "fantastic generalization measures and where to find them"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "functional variational bayesian neural networks"
    - "detecting extrapolation with local ensembles"
    - "semi flat minima and saddle points by embedding neural networks to overparameterization"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-4 (induced by source paper "self adaptive training beyond empirical risk minimization"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "self learning to filter noisy labels with self ensembling"
    - "selectivenet a deep neural network with an integrated reject option"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-5 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks"):
  with members:
    - "deep network approximation for smooth functions"
    - "the troublesome kernel why deep learning for inverse problems is typically unstable"
    - "greedy shallow networks a new approach for constructing and training neural networks"
    - "growing axons greedy learning of neural networks with application to function approximation"
    - "a polynomial based approach for architectural design and learning with deep neural networks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "neural network integral representations with the relu activation function"
    - "energy and policy considerations for deep learning in nlp"

Connected through hyperedge HE-6 (induced by source paper "the curious case of adversarially robust models more data can help double descend or hurt generalization"):
  with members:
    - "more data can expand the generalization gap between adversarially robust and standard models"
    - "a new perspective for flexible feature gathering in scene text recognition via character anchor pooling"
    - "lower bounds for adversarially robust pac learning"
    - "two models of double descent for weak features"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-7 (induced by source paper "do we need zero training loss after achieving zero training error"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "pumpout a meta approach to robust deep learning with noisy labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning for classical japanese literature"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-8 (induced by source paper "self explaining ai as an alternative to interpretable ai"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "identifying weights and architectures of unknown relu networks"
    - "on the in fidelity and sensitivity for explanations"
    - "knowing what you know in brain segmentation using bayesian deep neural networks"
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "sanity checks for saliency maps"

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)