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 pac bayesian approach to spectrally normalized margin bounds for neural networks"
  Abstract: We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (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"
    - "two models of double descent for weak features"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "towards understanding the spectral bias of deep learning"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "over parameterized two level neural networks can learn near optimal feature representations"

Connected through hyperedge HE-4 (induced by source paper "generalization and representational limits of graph neural networks"):
  with members:
    - "directional message passing for molecular graphs"
    - "efficient probabilistic logic reasoning with graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "graph transformer networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "a survey on graph kernels"

Connected through hyperedge HE-5 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "tensor contraction layers for parsimonious deep nets"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-6 (induced by source paper "the role of invariance in spectral complexity based generalization bounds"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "information matrices and generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-7 (induced by source paper "de randomized pac bayes margin bounds applications to non convex and non smooth predictors"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "algorithm dependent generalization bounds for overparameterized deep residual networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-8 (induced by source paper "on the generalization of bayesian deep nets for multi class classification"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"
    - "size independent sample complexity of neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"

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)