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: "generalization error in deep learning"
  Abstract: Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this chapter, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.

Connected through hyperedge HE-1 (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"
    - "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"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-2 (induced by source paper "lautum regularization for semi supervised transfer learning"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "taskonomy disentangling task transfer learning"
    - "a survey on deep transfer learning"
    - "semi supervised learning with declaratively specified entropy constraints"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "when semi supervised learning meets transfer learning training strategies models and datasets"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "deep learning for inverse problems bounds and regularizers"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "generalizable adversarial training via spectral normalization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "implicit regularization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "towards quantifying intrinsic generalization of deep relu networks"):
  with members:
    - "interpretable deep neural networks for patient mortality prediction a consensus based approach"
    - "detecting memorization in relu networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep model reference adaptive control"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"
    - "feature learning in deep neural networks studies on speech recognition tasks"
    - "robustness and generalization"

Connected through hyperedge HE-6 (induced by source paper "frequency principle in deep learning with general loss functions and its potential application"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "understanding deep learning requires rethinking generalization"

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)