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: "evolutionary neural automl for deep learning"
  Abstract: Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a configuration is difficult and as a result, DNNs are often not used to their full potential. In addition, DNNs in commercial applications often need to satisfy real-world design constraints such as size or number of parameters. To make configuration easier, automatic machine learning (AutoML) systems for deep learning have been developed, focusing mostly on optimization of hyperparameters. #R##N#This paper takes AutoML a step further. It introduces an evolutionary AutoML framework called LEAF that not only optimizes hyperparameters but also network architectures and the size of the network. LEAF makes use of both state-of-the-art evolutionary algorithms (EAs) and distributed computing frameworks. Experimental results on medical image classification and natural language analysis show that the framework can be used to achieve state-of-the-art performance. In particular, LEAF demonstrates that architecture optimization provides a significant boost over hyperparameter optimization, and that networks can be minimized at the same time with little drop in performance. LEAF therefore forms a foundation for democratizing and improving AI, as well as making AI practical in future applications.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of neural network architectures using codeepneat and keras"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "a survey on deep learning in medical image analysis"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-2 (induced by source paper "multi criterion evolutionary design of deep convolutional neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "evolutionary architecture search for deep multitask networks"

Connected through hyperedge HE-3 (induced by source paper "evolving robust neural architectures to defend from adversarial attacks"):
  with members:
    - "evolving deep neural networks by multi objective particle swarm optimization for image classification"
    - "one pixel attack for fooling deep neural networks"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses"
    - "adversarial patch"
    - "evolutionary architecture search for deep multitask networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "scalable reinforcement learning based neural architecture search for cancer deep learning research"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "balsam automated scheduling and execution of dynamic data intensive hpc workflows"
    - "evolutionary neural architecture search for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "automatic design of artificial neural networks for gamma ray detection"):
  with members:
    - "denser deep evolutionary network structured representation"
    - "fast denser evolving fully trained deep artificial neural networks"
    - "evolving deep neural networks"
    - "towards the evolution of multi layered neural networks a dynamic structured grammatical evolution approach"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "adam a method for stochastic optimization"
    - "genetic algorithms for evolving deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "automatic model monitoring for data streams"):
  with members:
    - "gradient boosted feature selection"
    - "ml health fitness tracking for production models"
    - "towards automl in the presence of drift first results"
    - "request and reverify hierarchical hypothesis testing for concept drift detection with expensive labels"
    - "adanet adaptive structural learning of artificial neural networks"

Connected through hyperedge HE-7 (induced by source paper "alwann automatic layer wise approximation of deep neural network accelerators without retraining"):
  with members:
    - "autoax an automatic design space exploration and circuit building methodology utilizing libraries of approximate components"
    - "evolving deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep residual learning for image recognition"

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)