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: "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
  Abstract: The ability to rank models by its real strength is the key to Neural Architecture Search. Traditional approaches adopt an incomplete training for such purpose which is still very costly. One-shot methods are thus devised to cut the expense by reusing the same set of weights. However, it is uncertain whether shared weights are truly effective. It is also unclear if a picked model is better because of its vigorous representational power or simply because it is overtrained. In order to remove the suspicion, we propose a novel idea called Fair Neural Architecture Search (FairNAS), in which a strict fairness constraint is enforced for fair inheritance and training. In this way, our supernet exhibits nice convergence and very high training accuracy. The performance of any sampled model loaded with shared weights from the supernet strongly correlates with that of stand-alone counterpart when trained fully. This result dramatically improves the searching efficiency, with a multi-objective reinforced evolutionary search backend, our pipeline generated a new set of state-of-the-art architectures on ImageNet: FairNAS-A attains 75.34% top-1 validation accuracy on ImageNet, FairNAS-B 75.10%, FairNAS-C 74.69%, even with lower multi-adds and/or fewer number of parameters compared with others. The models and their evaluation code are made publicly available online this http URL.

Connected through hyperedge HE-1 (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-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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "federated learning strategies for improving communication efficiency"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (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-4 (induced by source paper "adwpnas architecture driven weight prediction for neural architecture search"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "neural inheritance relation guided one shot layer assignment search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoslim towards one shot architecture search for channel numbers"
    - "mfas multimodal fusion architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "dsnas direct neural architecture search without parameter retraining"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "practical deep learning architecture optimization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-7 (induced by source paper "ponas progressive one shot neural architecture search for very efficient deployment"):
  with members:
    - "hm nas efficient neural architecture search via hierarchical masking"
    - "efficient differentiable neural architecture search with meta kernels"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "to share or not to share a comprehensive appraisal of weight sharing"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "deeper insights into weight sharing in neural architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "a survey on neural architecture search"
    - "understanding and improving one shot neural architecture optimization"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "regularized evolution for image classifier 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)