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 fcos fast neural architecture search for object detection"
  Abstract: The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.

Connected through hyperedge HE-1 (induced by source paper "econas finding proxies for economical neural architecture search"):
  with members:
    - "detnas backbone search for object detection"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "are all training examples created equal an empirical study"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "memory efficient hierarchical neural architecture search for image denoising"):
  with members:
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "graph hypernetworks for neural architecture search"
    - "toward convolutional blind denoising of real photographs"

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"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "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 "side aware boundary localization for more precise object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "switchable whitening for deep representation learning"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"

Connected through hyperedge HE-5 (induced by source paper "computation reallocation for object detection"):
  with members:
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "detnas backbone search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "scale aware trident networks for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "detnet a backbone network for object detection"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "grid r cnn"

Connected through hyperedge HE-6 (induced by source paper "mnasfpn learning latency aware pyramid architecture for object detection on mobile devices"):
  with members:
    - "thundernet towards real time generic object detection"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "panoptic feature pyramid networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "training time friendly network for real time object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-8 (induced by source paper "degas differentiable efficient generator search"):
  with members:
    - "agan towards automated design of generative adversarial networks"
    - "autogan neural architecture search for generative adversarial networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

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)