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: "partial order pruning for best speed accuracy trade off in neural architecture search"
  Abstract: Achieving good speed and accuracy trade-off on a target platform is very important in deploying deep neural networks in real world scenarios. However, most existing automatic architecture search approaches only concentrate on high performance. In this work, we propose an algorithm that can offer better speed/accuracy trade-off of searched networks, which is termed "Partial Order Pruning". It prunes the architecture search space with a partial order assumption to automatically search for the architectures with the best speed and accuracy trade-off. Our algorithm explicitly takes profile information about the inference speed on the target platform into consideration. With the proposed algorithm, we present several Dongfeng (DF) networks that provide high accuracy and fast inference speed on various application GPU platforms. By further searching decoder architectures, our DF-Seg real-time segmentation networks yield state-of-the-art speed/accuracy trade-off on both the target embedded device and the high-end GPU.

Connected through hyperedge HE-1 (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"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "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-2 (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"
    - "nas evaluation is frustratingly hard"
    - "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-3 (induced by source paper "sm nas structural to modular neural architecture search for object detection"):
  with members:
    - "thundernet towards real time generic object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "revisiting feature alignment for one stage object detection"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "efficient differentiable neural architecture search with meta kernels"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for a robust neural architecture in four gpu hours"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "densely connected search space for more flexible neural architecture search"
    - "searching for mobilenetv3"

Connected through hyperedge HE-5 (induced by source paper "searching for a robust neural architecture in four gpu hours"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-6 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "autoqb automl for network quantization and binarization on mobile devices"

Connected through hyperedge HE-7 (induced by source paper "fasterseg searching for faster real time semantic segmentation"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "squeezenas fast neural architecture search for faster semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "neural epitome search for architecture agnostic network compression"):
  with members:
    - "low bit quantization of neural networks for efficient inference"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"

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)