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: "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
  Abstract: We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks. Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped). Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

Connected through hyperedge HE-1 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "huggingface s transformers state of the art natural language processing"
    - "pytorch an imperative style high performance deep learning library"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "the singular values of convolutional layers"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "towards efficient training for neural network quantization"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "a mean field theory of batch normalization"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "fixup initialization residual learning without normalization"
    - "learned step size quantization"

Connected through hyperedge HE-3 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "information theoretic thresholds from the cavity method"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-4 (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"
    - "theory iiib generalization in deep networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "understanding generalization and optimization performance of deep cnns"

Connected through hyperedge HE-5 (induced by source paper "traditional and heavy tailed self regularization in neural network models"):
  with members:
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularization for deep learning a taxonomy"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-6 (induced by source paper "parameter re initialization through cyclical batch size schedules"):
  with members:
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "three factors influencing minima in sgd"
    - "integrated model batch and domain parallelism in training neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "don t decay the learning rate increase the batch size"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-7 (induced by source paper "toward convolutional blind denoising of real photographs"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "noise2noise learning image restoration without clean data"
    - "multi level wavelet cnn for image restoration"
    - "chaining identity mapping modules for image denoising"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-8 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "decoding from pooled data phase transitions of message passing"
    - "performance limits for noisy multimeasurement vector problems"
    - "spurious local minima are common in two layer relu neural networks"
    - "mutual information and optimality of approximate message passing in random linear estimation"

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)