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: "customizable architecture search for semantic segmentation"
  Abstract: In this paper, we propose a Customizable Architecture Search (CAS) approach to automatically generate a network architecture for semantic image segmentation. The generated network consists of a sequence of stacked computation cells. A computation cell is represented as a directed acyclic graph, in which each node is a hidden representation (i.e., feature map) and each edge is associated with an operation (e.g., convolution and pooling), which transforms data to a new layer. During the training, the CAS algorithm explores the search space for an optimized computation cell to build a network. The cells of the same type share one architecture but with different weights. In real applications, however, an optimization may need to be conducted under some constraints such as GPU time and model size. To this end, a cost corresponding to the constraint will be assigned to each operation. When an operation is selected during the search, its associated cost will be added to the objective. As a result, our CAS is able to search an optimized architecture with customized constraints. The approach has been thoroughly evaluated on Cityscapes and CamVid datasets, and demonstrates superior performance over several state-of-the-art techniques. More remarkably, our CAS achieves 72.3% mIoU on the Cityscapes dataset with speed of 108 FPS on an Nvidia TitanXp GPU.

Connected through hyperedge HE-1 (induced by source paper "fast neural network adaptation via parameter remapping and architecture search"):
  with members:
    - "eat nas elastic architecture transfer for accelerating large scale neural architecture search"
    - "densely connected search space for more flexible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training time friendly network for real time object detection"
    - "rethinking imagenet pre training"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "semantic flow for fast and accurate scene parsing"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "guided upsampling network for real time semantic segmentation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "low latency video semantic segmentation"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"

Connected through hyperedge HE-3 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "video action recognition via neural architecture searching"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural architecture search for deep face recognition"
    - "searching for a robust neural architecture in four gpu hours"
    - "auto reid searching for a part aware convnet for person re identification"
    - "gabor convolutional networks"

Connected through hyperedge HE-4 (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"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "evaluating the search phase of neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "adversarialnas adversarial neural architecture search for gans"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "agan towards automated design of generative adversarial networks"
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "autogan neural architecture search for generative adversarial networks"
    - "one shot neural architecture search via self evaluated template network"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "graph guided architecture search for real time semantic segmentation"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

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"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "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"

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)