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 "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"):
  Abstract: Recurrent neural networks have demonstrated to be good at solving prediction problems. However, finding a network that suits a problem is quite hard because of their high sensitivity to the hyperparameter configuration. Automatic hyperparameter optimization methods help to find the most suitable configuration, but they are not extensively adopted because of their high computational cost. In this work, we study the use of the mean absolute error random sampling to compare multiple-hidden-layer architectures and propose an evolutionary strategy-based algorithm that uses its results to optimize the configuration of a recurrent network. We empirically validate our proposal and show that it is possible to predict and compare the expected performance of a hyperparameter configuration in a low-cost way, as well as use these predictions to optimize the configuration of a recurrent network.

Includes members:
  Member "bin ct urban waste collection based on predicting the container fill level":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "low cost recurrent neural network expected performance evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "evolutionary architecture search for deep multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deeper insights into weight sharing in neural architecture search")
  Member "dlopt deep learning optimization library":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low cost recurrent neural network expected performance evaluation")
  Member "evolving deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "medgan medical image translation using gans")
  Member "metaheuristic design of feedforward neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian neural architecture search using a training free performance metric")
  Member "metaheuristic algorithms for convolution neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimization of convolutional neural network using the linearly decreasing weight particle swarm optimization")

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)