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: "asymmetric valleys beyond sharp and flat local minima"
  Abstract: Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which have been proposed to be associated with good generalization performance. In this paper, we observe that local minima of modern deep networks are more than being flat or sharp. Specifically, at a local minimum there exist many asymmetric directions such that the loss increases abruptly along one side, and slowly along the opposite side--we formally define such minima as asymmetric valleys. Under mild assumptions, we prove that for asymmetric valleys, a solution biased towards the flat side generalizes better than the exact minimizer. Further, we show that simply averaging the weights along the SGD trajectory gives rise to such biased solutions implicitly. This provides a theoretical explanation for the intriguing phenomenon observed by Izmailov et al. (2018). In addition, we empirically find that batch normalization (BN) appears to be a major cause for asymmetric valleys.

Connected through hyperedge HE-1 (induced by source paper "automatic perturbation analysis on general computational graphs"):
  with members:
    - "robustness verification for transformers"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "fastened crown tightened neural network robustness certificates"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "popqorn quantifying robustness of recurrent neural networks"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-2 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "where is the information in a deep neural network"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "don t decay the learning rate increase the batch size"
    - "sharp minima can generalize for deep nets"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning accurate integer transformer machine translation models"):
  with members:
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "training tips for the transformer model"
    - "marian cost effective high quality neural machine translation in c"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)