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: "convolutional neural fabrics"
  Abstract: Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a "fabric" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset.

Connected through hyperedge HE-1 (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"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-2 (induced by source paper "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"):
  with members:
    - "nas bench 102 extending the scope of reproducible neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "best practices for scientific research on neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"

Connected through hyperedge HE-3 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-4 (induced by source paper "pixel level dense prediction without decoder"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "autodispnet improving disparity estimation with automl"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "learning to design rna"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "flattenet a simple versatile framework for dense pixelwise prediction"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-7 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "pose neural fabrics search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "objects as points"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "regularized evolution for image classifier 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)