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: "neural networks designing neural networks multi objective hyper parameter optimization"
  Abstract: Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition, and automated control. Both the performance and computational complexity of such models are heavily dependant on the design of characteristic hyper-parameters (e.g., number of hidden layers, nodes per layer, or choice of activation functions), which have traditionally been optimized manually. With machine learning penetrating low-power mobile and embedded areas, the need to optimize not only for performance (accuracy), but also for implementation complexity, becomes paramount. In this work, we present a multi-objective design space exploration method that reduces the number of solution networks trained and evaluated through response surface modelling. Given spaces which can easily exceed 1020 solutions, manually designing a near-optimal architecture is unlikely as opportunities to reduce network complexity, while maintaining performance, may be overlooked. This problem is exacerbated by the fact that hyper-parameters which perform well on specific datasets may yield sub-par results on others, and must therefore be designed on a per-application basis. In our work, machine learning is leveraged by training an artificial neural network to predict the performance of future candidate networks. The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity. Experimental results demonstrate that the proposed method can closely approximate the Pareto-optimal front, while only exploring a small fraction of the design space.

Connected through hyperedge HE-1 (induced by source paper "rapdarts resource aware progressive differentiable architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "a survey on neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "regularized evolution for image classifier architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "hypernomad hyperparameter optimization of deep neural networks using mesh adaptive direct search"):
  with members:
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "black box optimization in machine learning with trust region based derivative free algorithm"
    - "a survey on deep learning in medical image analysis"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-3 (induced by source paper "toco a framework for compressing neural network models based on tolerance analysis"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "evolving deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "towards the limit of network quantization"
    - "pruning convolutional neural networks for resource efficient inference"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-4 (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"
    - "low cost recurrent neural network expected performance evaluation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "deep learning for detecting building defects using convolutional neural networks"):
  with members:
    - "particle swarm optimization model to predict scour depth around bridge pier"
    - "object detection with deep learning a review"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "deep convolutional networks for pancreas segmentation in ct imaging"
    - "learning deep features for discriminative localization"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "pabo pseudo agent based multi objective bayesian hyperparameter optimization for efficient neural accelerator design"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "dvolver efficient pareto optimal neural network architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "searching toward pareto optimal device aware neural architectures"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reinforced evolutionary neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "auto keras an efficient neural architecture search system"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-8 (induced by source paper "agent embeddings a latent representation for pole balancing networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "visual interpretability for deep learning a survey"
    - "programmatically interpretable reinforcement learning"
    - "policy certificates towards accountable reinforcement learning"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "dna gan learning disentangled representations from multi attribute images"
    - "recognizing plans by learning embeddings from observed action distributions"

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.NE(Neural and Evolutionary Computing)