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: "progressive differentiable architecture search bridging the depth gap between search and evaluation"
  Abstract: Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search. However, these approaches often report lower accuracy in evaluating the searched architecture or transferring it to another dataset. This is arguably due to the large gap between the architecture depths in search and evaluation scenarios. In this paper, we present an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. This brings two issues, namely, heavier computational overheads and weaker search stability, which we solve using search space approximation and regularization, respectively. With a significantly reduced search time (~7 hours on a single GPU), our approach achieves state-of-the-art performance on both the proxy dataset (CIFAR10 or CIFAR100) and the target dataset (ImageNet). Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "latent weights do not exist rethinking binarized neural network optimization"

Connected through hyperedge HE-2 (induced by source paper "nas count counting by density with neural 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"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"

Connected through hyperedge HE-3 (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-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"
    - "bayesnas a bayesian approach for neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

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"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-7 (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"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "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-8 (induced by source paper "multi objective neural architecture search via non stationary policy gradient"):
  with members:
    - "nas evaluation is frustratingly hard"
    - "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"

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.CV(Computer Vision and Pattern Recognition)