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 "on the power of curriculum learning in training deep networks"):
  Abstract: Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non-uniform sampling of mini-batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini-batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive ``teacher" network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR-10, CIFAR-100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.

Includes members:
  Member "curriculum learning by transfer learning theory and experiments with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "learning to teach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative teaching networks accelerating neural architecture search by learning to generate synthetic training data")
  Member "automated curriculum learning for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "reverse curriculum generation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "mentornet learning data driven curriculum for very deep neural networks on corrupted labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep speech 2 end to end speech recognition in english and mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  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)