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: "densely connected search space for more flexible neural architecture search"
  Abstract: In recent years, neural architecture search (NAS) has dramatically advanced the development of neural network design. While most previous works are computationally intensive, differentiable NAS methods reduce the search cost by constructing a super network in a continuous space covering all possible architectures to search for. However, few of them can search for the network width (the number of filters/channels) because it is intractable to integrate architectures with different widths into one super network following conventional differentiable NAS paradigm. In this paper, we propose a novel differentiable NAS method which can search for the width and the spatial resolution of each block simultaneously. We achieve this by constructing a densely connected search space and name our method as DenseNAS. Blocks with different width and spatial resolution combinations are densely connected to each other. The best path in the super network is selected by optimizing the transition probabilities between blocks. As a result, the overall depth distribution of the network is optimized globally in a graceful manner. In the experiments, DenseNAS obtains an architecture with 75.9% top-1 accuracy on ImageNet and the latency is as low as 24.3ms on a single TITAN-XP. The total search time is merely 23 hours on 4 GPUs.

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"
    - "customizable architecture search for semantic segmentation"
    - "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 "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"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"

Connected through hyperedge HE-3 (induced by source paper "one shot neural architecture search via self evaluated template network"):
  with members:
    - "network pruning via transformable architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "nas bench 101 towards reproducible neural architecture search"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reinforced evolutionary neural architecture search"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "auto reid searching for a part aware convnet for person re identification"):
  with members:
    - "adaptive exploration for unsupervised person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "searching for a robust neural architecture in four gpu hours"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "atomnas fine grained end to end neural architecture search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "single path one shot neural architecture search with uniform sampling"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "single path mobile automl efficient convnet design and nas hyperparameter optimization"

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)