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 "pal a fast dnn optimization method based on curvature information"):
  Abstract: We present a novel optimizer for deep neural networks that combines the ideas of Netwon's method and line search to efficiently compute and utilize curvature information. Our work is based on empirical observation suggesting that the loss function can be approximated by a parabola in negative gradient direction. Due to this approximation, we are able to perform a variable and loss function dependent parameter update by jumping directly into the minimum of the approximated parabola. To evaluate our optimizer, we performed multiple comprehensive hyperparameter grid searches for which we trained more than 20000 networks in total. We can show that PAL outperforms RMSPROP, and can outperform gradient descent with momentum and ADAM on large-scale high-dimensional machine learning problems. Furthermore, PAL requires up to 52.2% less training epochs. PyTorch and TensorFlow implementations are provided at this https URL.

Includes members:
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a metric learning reality check")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "l4 practical loss based stepsize adaptation for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative sampling in generative adversarial networks")
  Member "shakedrop regularization for deep residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "shake shake regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimized generic feature learning for few shot classification across domains")
  Member "neither quick nor proper evaluation of quickprop for learning deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parabolic approximation line search an efficient and effective line search approach for dnns")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)