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: "a genetic programming approach to designing convolutional neural network architectures"
  Abstract: The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "real time federated evolutionary neural architecture search"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "searching for a robust neural architecture in four gpu hours"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "single path one shot neural architecture search with uniform sampling"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "autoq automated kernel wise neural network quantization"):
  with members:
    - "optimizing bit serial matrix multiplication for reconfigurable computing"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-4 (induced by source paper "multi criterion evolutionary design of deep convolutional neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "progressive darts bridging the optimization gap for nas in the wild"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "centernet keypoint triplets for object detection"
    - "searching for a robust neural architecture in four gpu hours"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "stabilizing darts with amended gradient estimation on architectural parameters"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (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"
    - "evolutionary neural automl for deep learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "progressive differentiable architecture search bridging the depth gap between search and evaluation"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "immunecs neural committee search by an artificial immune system"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deepswarm optimising convolutional neural networks using swarm intelligence"
    - "evaluating the search phase of neural architecture search"
    - "best practices for scientific research on neural architecture search"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "auto keras an efficient neural architecture search system"

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)