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: "theory iiib generalization in deep networks"
  Abstract: A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when increasing the number of neurons or of iterations of gradient descent. This is surprising because of the large capacity demonstrated by DNNs to fit randomly labeled data and the absence of explicit regularization. Recent results by Srebro et al. provide a satisfying solution of the puzzle for linear networks used in binary classification. They prove that minimization of loss functions such as the logistic, the cross-entropy and the exp-loss yields asymptotic, "slow" convergence to the maximum margin solution for linearly separable datasets, independently of the initial conditions. Here we prove a similar result for nonlinear multilayer DNNs near zero minima of the empirical loss. The result holds for exponential-type losses but not for the square loss. In particular, we prove that the weight matrix at each layer of a deep network converges to a minimum norm solution up to a scale factor (in the separable case). Our analysis of the dynamical system corresponding to gradient descent of a multilayer network suggests a simple criterion for ranking the generalization performance of different zero minimizers of the empirical loss.

Connected through hyperedge HE-1 (induced by source paper "think global act local relating dnn generalisation and node level snr"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "tightening mutual information based bounds on generalization error"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "on variational bounds of mutual information"
    - "entropy and mutual information in models of deep neural networks"
    - "putting an end to end to end gradient isolated learning of representations"
    - "chaining mutual information and tightening generalization bounds"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "traditional and heavy tailed self regularization in neural network models"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "understanding generalization and optimization performance of deep cnns"

Connected through hyperedge HE-3 (induced by source paper "layer dynamics of linearised neural nets"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "lstm a search space odyssey"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "theory ii landscape of the empirical risk in deep learning"
    - "identity matters in deep learning"
    - "dynamics of stochastic gradient algorithms"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-4 (induced by source paper "how implicit regularization of neural networks affects the learned function part i"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "implicit regularization in over parameterized neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "implicit regularization in deep learning"
    - "adam a method for stochastic optimization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"
    - "a capacity scaling law for artificial neural networks"

Connected through hyperedge HE-6 (induced by source paper "measure manifold learning and optimization a theory of neural networks"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "expectation propagation a probabilistic view of deep feed forward networks"
    - "optimization landscape and expressivity of deep cnns"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "searching for activation functions"
    - "the loss surface of deep and wide neural networks"
    - "global optimality conditions for deep neural networks"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-7 (induced by source paper "an analysis of training and generalization errors in shallow and deep networks"):
  with members:
    - "exploring generalization in deep learning"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "a deep learning approach to diabetic blood glucose prediction"
    - "understanding deep learning requires rethinking generalization"
    - "deep vs shallow networks an approximation theory perspective"
    - "train faster generalize better stability of stochastic gradient descent"
    - "eignets for function approximation on manifolds"

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)