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: "scalable gradient based tuning of continuous regularization hyperparameters"
  Abstract: Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for locally adjusting hyperparameters during training of the model. Hyperparameters are adjusted so as to make the model parameter gradients, and hence updates, more advantageous for the validation cost. We explore the approach for tuning regularization hyperparameters and find that in experiments on MNIST, SVHN and CIFAR-10, the resulting regularization levels are within the optimal regions. The additional computational cost depends on how frequently the hyperparameters are trained, but the tested scheme adds only 30% computational overhead regardless of the model size. Since the method is significantly less computationally demanding compared to similar gradient-based approaches to hyperparameter optimization, and consistently finds good hyperparameter values, it can be a useful tool for training neural network models.

Connected through hyperedge HE-1 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "an ensemble of epoch wise empirical bayes for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "meta transfer learning through hard tasks"
    - "a closer look at few shot classification"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

Connected through hyperedge HE-3 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "meta learning with implicit gradients"
    - "deep equilibrium models"
    - "autoassist a framework to accelerate training of deep neural networks"
    - "generalized inner loop meta learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "adversarial learning of general transformations for data augmentation"

Connected through hyperedge HE-4 (induced by source paper "metamixup learning adaptive interpolation policy of mixup with meta learning"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"
    - "mixup as locally linear out of manifold regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "progressive neural architecture search"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "deep reinforcement learning that matters"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning to customize and combine deep learners for few shot learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "learning embedding adaptation for few shot learning"
    - "meta learning update rules for unsupervised representation learning"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "searching to exploit memorization effect in learning from corrupted labels"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "how does disagreement help generalization against label corruption"
    - "searching for interaction functions in collaborative filtering"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "taking human out of learning applications a survey on automated machine learning"
    - "dimensionality driven learning with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "generalized inner loop meta learning"):
  with members:
    - "meta learning via learned loss"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "tadam task dependent adaptive metric for improved few shot learning"

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)