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: "evaluating the search phase of neural architecture search"
  Abstract: Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently evaluated solely by comparing their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we present a NAS evaluation framework that includes the search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the random policy outperforms state-of-the-art NAS algorithms; (ii) The results and candidate rankings of NAS algorithms do not reflect the true performance of the candidate architectures; and (iii) The widely used weight sharing strategy negatively impacts the training of good architectures, thus reducing the effectiveness of the search process. We believe that following our evaluation framework will be key to designing NAS strategies that truly discover superior architectures.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sgas sequential greedy architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "kpconv flexible and deformable convolution for point clouds"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "bayesnas a bayesian approach for neural architecture search"
    - "fast graph representation learning with pytorch geometric"
    - "deepgcns can gcns go as deep as cnns"
    - "deepgcns making gcns go as deep as cnns"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-3 (induced by source paper "densely connected search space for more flexible neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "network pruning via transformable architecture search"
    - "vargnet variable group convolutional neural network for efficient embedded computing"
    - "exploring randomly wired neural networks for image recognition"
    - "customizable architecture search for semantic segmentation"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-4 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "weight agnostic neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "continual lifelong learning with neural networks a review"
    - "the evolved transformer"
    - "reinforcement learning for improving agent design"
    - "measuring the intrinsic dimension of objective landscapes"
    - "regularized evolution for image classifier architecture search"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-6 (induced by source paper "architecture search by estimation of network structure distributions"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "operational neural networks"
    - "instanas instance aware neural architecture search"
    - "finding better topologies for deep convolutional neural networks by evolution"
    - "regularized evolution for image classifier architecture search"
    - "methods for interpreting and understanding deep neural networks"
    - "darts differentiable architecture search"
    - "understanding batch normalization"

Connected through hyperedge HE-7 (induced by source paper "fair darts eliminating unfair advantages in differentiable architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "xnas neural architecture search with expert advice"

Connected through hyperedge HE-8 (induced by source paper "xnas neural architecture search with expert advice"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "introduction to online convex optimization"
    - "exploring randomly wired neural networks for image recognition"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "on the convergence of adam and beyond"
    - "cinic 10 is not imagenet or cifar 10"
    - "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)