human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bad global minima exist and sgd can reach them"):
  Abstract: Several recent works have aimed to explain why severely overparameterized models, generalize well when trained by Stochastic Gradient Descent (SGD). The emergent consensus explanation has two parts: the first is that there are "no bad local minima", while the second is that SGD performs implicit regularization by having a bias towards low complexity models. We revisit both of these ideas in the context of image classification with common deep neural network architectures. Our first finding is that there exist bad global minima, i.e., models that fit the training set perfectly, yet have poor generalization. Our second finding is that given only unlabeled training data, we can easily construct initializations that will cause SGD to quickly converge to such bad global minima. For example, on CIFAR, CINIC10, and (Restricted) ImageNet, this can be achieved by starting SGD at a model derived by fitting random labels on the training data: while subsequent SGD training (with the correct labels) will reach zero training error, the resulting model will exhibit a test accuracy degradation of up to 40% compared to training from a random initialization. Finally, we show that regularization seems to provide SGD with an escape route: once heuristics such as data augmentation are used, starting from a complex model (adversarial initialization) has no effect on the test accuracy.

Includes members:
  Member "cinic 10 is not imagenet or cifar 10":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dhog deep hierarchical object grouping")
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "implicit regularization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models")
  Member "geometry of optimization and implicit regularization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "double trouble in double descent bias and variance s in the lazy regime")
  Member "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an image enhancing pattern based sparsity for real time inference on mobile devices")
  Member "sgd learns over parameterized networks that provably generalize on linearly separable data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the global convergence of training deep linear resnets")
  Member "theory of deep learning iii explaining the non overfitting puzzle":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "removable and or repeated units emerge in overparametrized deep neural networks")
  Member "generalization properties and implicit regularization for multiple passes sgm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "increasing generality in machine learning through procedural content generation")

Question: Please tell me which class the source paper 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)