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: "nas evaluation is frustratingly hard"
  Abstract: Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue. While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all. As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others. Our first contribution is a benchmark of $8$ NAS methods on $5$ datasets. To overcome the hurdle of comparing methods with different search spaces, we propose using a method's relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols. Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline. We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline. These experiments highlight that: (i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures; (ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings; (iii) the hand-designed macro-structure (cells) is more important than the searched micro-structure (operations); and (iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between $8$ and $20$ cell architectures. To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls. The code used is available at this https URL.

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"
    - "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"
    - "atomnas fine grained end to end neural architecture search"

Connected through hyperedge HE-2 (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"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "understanding and robustifying differentiable architecture search"
    - "assemblenet searching for multi stream neural connectivity in video architectures"

Connected through hyperedge HE-3 (induced by source paper "multi objective neural architecture search via non stationary policy gradient"):
  with members:
    - "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"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "efficient sample based neural architecture search with learnable predictor"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "best practices for scientific research on neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "blockwisely supervised neural architecture search with knowledge distillation"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "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"
    - "mixconv mixed depthwise convolutional kernels"
    - "improving one shot nas by suppressing the posterior fading"
    - "single path one shot neural architecture search with uniform sampling"
    - "moga searching beyond mobilenetv3"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "manas multi agent neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "snas stochastic neural 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)