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: "two models of double descent for weak features"
  Abstract: The "double descent" risk curve was recently proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provides a precise mathematical analysis for the shape of this curve in two simple data models with the least squares/least norm predictor. Specifically, it is shown that the risk peaks when the number of features $p$ is close to the sample size $n$, but also that the risk decreases towards its minimum as $p$ increases beyond $n$. This behavior is contrasted with that of "prescient" models that select features in an a priori optimal order.

Connected through hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"
    - "is deeper better only when shallow is good"

Connected through hyperedge HE-2 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited"):
  with members:
    - "fantastic generalization measures and where to find them"
    - "harmless interpolation of noisy data in regression"
    - "deep double descent where bigger models and more data hurt"
    - "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-3 (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"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "deep double descent where bigger models and more data hurt"
    - "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-4 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "greedy column subset selection new bounds and distributed algorithms"

Connected through hyperedge HE-5 (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"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "deep double descent where bigger models and more data hurt"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "kernel and deep regimes in overparametrized models"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "deep double descent where bigger models and more data hurt"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-7 (induced by source paper "optimal regularization can mitigate double descent"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "harmless interpolation of noisy data in regression"
    - "deep double descent where bigger models and more data hurt"
    - "minimizers of the empirical risk and risk monotonicity"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-8 (induced by source paper "exploring the memorization generalization continuum in deep learning"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "does learning require memorization a short tale about a long tail"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "improving generalization performance by switching from adam to sgd"

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)