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 "is the meta learning idea able to improve the generalization of deep neural networks on the standard supervised learning"):
  Abstract: Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few-shot learning is more challenging than the standard supervised learning as each target class only has a very few or no training samples. The natural question that arises is whether the meta-learning idea can be used for improving the generalization of DNNs on the standard supervised learning. In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs. MLTP simulates the meta-training process by considering a batch of training samples as a task. The key idea is that the gradient descent step for improving the current task performance should also improve a new task performance, which is ignored by the current standard procedure for training neural networks. MLTP also benefits from all the existing training techniques such as dropout, weight decay, and batch normalization. We evaluate MLTP by training a variety of small and large neural networks on three benchmark datasets, i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet. The experimental results show a consistently improved generalization performance on all the DNNs with different sizes, which verifies the promise of MLTP and demonstrates that the meta-learning idea is indeed able to improve the generalization of DNNs on the standard supervised learning.

Includes members:
  Member "alpha maml adaptive model agnostic meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "personalized federated learning a meta learning approach")
  Member "on first order meta learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "training behavior of deep neural network in frequency domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "margin maximization as lossless maximal compression")
  Member "recasting gradient based meta learning as hierarchical bayes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms")
  Member "bayesian model agnostic meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning for mixed linear regression")
  Member "how to train your maml":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "understanding training and generalization in deep learning by fourier analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "auto meta automated gradient based meta learner search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metadapt meta learned task adaptive architecture for few shot classification")

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)