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 architecture optimization"
  Abstract: Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. (2) A predictor takes the continuous representation of a network as input and predicts its accuracy. (3) A decoder maps a continuous representation of a network back to its architecture. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy. Such a better embedding is then decoded to a network by the decoder. Experiments show that the architecture discovered by our method is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources. Specifically we obtain 1.93% test set error rate for CIFAR-10 image classification task and 56.0 test set perplexity of PTB language modeling task. Furthermore, combined with the recent proposed weight sharing mechanism, we discover powerful architecture on CIFAR-10 (with error rate 2.93%) and on PTB (with test set perplexity 56.6), with very limited computational resources (less than 10 GPU hours) for both tasks.

Connected through hyperedge HE-1 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"

Connected through hyperedge HE-2 (induced by source paper "mixpath a unified approach for one shot neural architecture search"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "scarlet nas bridging the gap between stability and scalability in weight sharing neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "the evolved transformer"
    - "mixconv mixed depthwise convolutional kernels"

Connected through hyperedge HE-3 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "understanding architectures learnt by cell based neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "certified adversarial robustness via randomized smoothing"
    - "searching for a robust neural architecture in four gpu hours"
    - "fair darts eliminating unfair advantages in differentiable architecture search"
    - "understanding and robustifying differentiable architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "nas bench 201 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "understanding and robustifying differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "latency aware differentiable neural architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "semi supervised neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "neural predictor for neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "fastspeech fast robust and controllable text to speech"
    - "the evolved transformer"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-7 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "deep learning based recommender system a survey and new perspectives"
    - "regularized evolution for image classifier architecture search"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "multi objective neural architecture search via non stationary policy gradient"):
  with members:
    - "nas evaluation is frustratingly hard"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

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)