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 "using small proxy datasets to accelerate hyperparameter search"):
  Abstract: One of the biggest bottlenecks in a machine learning workflow is waiting for models to train. Depending on the available computing resources, it can take days to weeks to train a neural network on a large dataset with many classes such as ImageNet. For researchers experimenting with new algorithmic approaches, this is impractically time consuming and costly. We aim to generate smaller "proxy datasets" where experiments are cheaper to run but results are highly correlated with experimental results on the full dataset. We generate these proxy datasets using by randomly sampling from examples or classes, training on only the easiest or hardest examples and training on synthetic examples generated by "data distillation". We compare these techniques to the more widely used baseline of training on the full dataset for fewer epochs. For each proxying strategy, we estimate three measures of "proxy quality": how much of the variance in experimental results on the full dataset can be explained by experimental results on the proxy dataset. #R##N#Experiments on Imagenette and Imagewoof (Howard, 2019) show that running hyperparameter search on the easiest 10% of examples explains 81% of the variance in experiment results on the target task, and using the easiest 50% of examples can explain 95% of the variance, significantly more than training on all the data for fewer epochs, a more widely used baseline. These "easy" proxies are higher quality than training on the full dataset for a reduced number of epochs (but equivalent computational cost), and, unexpectedly, higher quality than proxies constructed from the hardest examples. Without access to a trained model, researchers can improve proxy quality by restricting the subset to fewer classes; proxies built on half the classes are higher quality than those with an equivalent number of examples spread across all classes.

Includes members:
  Member "dataset distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "not all samples are created equal deep learning with importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "no regret sample selection with noisy labels")
  Member "super convergence very fast training of residual networks using large learning rates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "regularizing neural networks by penalizing confident output distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "neural architecture search with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "are all training examples equally valuable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dataset distillation")

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)