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 "population based training for loss function optimization"):
  Abstract: Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. Loss functions are a type of metaknowledge that is crucial to effective training of DNNs and their potential role in metalearning has not yet been fully explored. This paper presents an algorithm called Enhanced Population-Based Training (EPBT) that interleaves the training of a DNN's weights with the metalearning of optimal hyperparameters and loss functions. Loss functions use a TaylorGLO parameterization, based on multivariate Taylor expansions, that EPBT can directly optimize. On the CIFAR-10 and SVHN image classification benchmarks, EPBT discovers loss function schedules that enable faster, more accurate learning. The discovered functions adapt to the training process and serve to regularize the learning task by discouraging overfitting to the labels. EPBT thus demonstrates a promising synergy of simultaneous training and metalearning.

Includes members:
  Member "evolving loss functions with multivariate taylor polynomial parameterizations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms")
  Member "visualising basins of attraction for the cross entropy and the squared error neural network loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "loss surface modality of feed forward neural network architectures")
  Member "population based augmentation efficient learning of augmentation policy schedules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "improved training speed accuracy and data utilization through loss function optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "searching for winograd aware quantized networks")
  Member "darts differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "nsga net neural architecture search using multi objective genetic algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas")

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.NE(Neural and Evolutionary Computing)