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: "low cost recurrent neural network expected performance evaluation"
  Abstract: Recurrent neural networks are strong dynamic systems, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough task, therefore selecting an appropriate configuration is critical. There have been proposed varied strategies to tackle this issue, however most of them are still impractical because of the time/resources needed. In this study, we propose a low computational cost model to evaluate the expected performance of a given architecture based on the distribution of the error of random samples. We validate empirically our proposal using three use case.

Connected through hyperedge HE-1 (induced by source paper "bayesian neural architecture search using a training free performance metric"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "bin ct urban waste collection based on predicting the container fill level"
    - "evolutionary architecture search for deep multitask networks"
    - "metaheuristic design of feedforward neural networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "an empirical exploration of deep recurrent connections and memory cells using neuro evolution"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "from nodes to networks evolving recurrent neural networks"
    - "evolving deep neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "learning simpler language models with the differential state framework"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-3 (induced by source paper "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"):
  with members:
    - "bin ct urban waste collection based on predicting the container fill level"
    - "evolutionary architecture search for deep multitask networks"
    - "dlopt deep learning optimization library"
    - "evolving deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "metaheuristic design of feedforward neural networks"
    - "metaheuristic algorithms for convolution neural network"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

Connected through hyperedge HE-4 (induced by source paper "the ant swarm neuro evolution procedure for optimizing recurrent networks"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "evolving deep neural networks"
    - "learning simpler language models with the differential state framework"
    - "large scale evolution of convolutional neural networks using volunteer computing"
    - "minimal gated unit for recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "dlopt deep learning optimization library"):
  with members:
    - "evolving deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

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)