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: "a jamming transition from under to over parametrization affects generalization in deep learning"
  Abstract: We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization. Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parametrized network, we observe that the generalization error displays three phases: (i) initial decay, (ii) increase until the transition point --- where it displays a cusp --- and (iii) slow decay toward a constant for the rest of the over-parametrized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.

Connected through hyperedge HE-1 (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"
    - "deep double descent where bigger models and more data hurt"
    - "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-2 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning"):
  with members:
    - "accelerating sgd with momentum for over parameterized learning"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "double descent in the condition number"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-3 (induced by source paper "self explaining ai as an alternative to interpretable ai"):
  with members:
    - "two models of double descent for weak features"
    - "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-4 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime"):
  with members:
    - "two models of double descent for weak features"
    - "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-5 (induced by source paper "optimal regularization can mitigate double descent"):
  with members:
    - "two models of double descent for weak features"
    - "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-6 (induced by source paper "rethinking bias variance trade off for generalization of neural networks"):
  with members:
    - "deep double descent where bigger models and more data hurt"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "large memory layers with product keys"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "cross lingual language model pretraining"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-8 (induced by source paper "luck matters understanding training dynamics of deep relu networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning is robust to massive label noise"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a theoretical framework for deep and locally connected relu network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

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)