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: "laplacian reconstruction and refinement for semantic segmentation"
  Abstract: CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation contains significant sub-pixel localization information. (2) We describe a multi-resolution reconstruction architecture, akin to a Laplacian pyramid, that uses skip connections from higher resolution feature maps to successively refine segment boundaries reconstructed from lower resolution maps. This approach yields state-of-the-art semantic segmentation results on PASCAL without resorting to more complex CRF or detection driven architectures.

Connected through hyperedge HE-1 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-2 (induced by source paper "residual pyramid learning for single shot semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "impatient dnns deep neural networks with dynamic time budgets"

Connected through hyperedge HE-3 (induced by source paper "nail polish try on realtime semantic segmentation of small objects for native and browser smartphone ar applications"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "loss max pooling for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "path aggregation network for instance segmentation"):
  with members:
    - "focal loss for dense object detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "understanding convolution for semantic segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic understanding of scenes through the ade20k dataset"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "optical flow with semantic segmentation and localized layers"
    - "mixed context networks for semantic segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "a projected gradient descent method for crf inference allowing end to end training of arbitrary pairwise potentials"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "efficient continuous relaxations for dense crf"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"
    - "structured prediction energy networks"

Connected through hyperedge HE-7 (induced by source paper "loss max pooling for semantic image segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pixelnet towards a general pixel level architecture"
    - "joint calibration for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-8 (induced by source paper "full resolution residual networks for semantic segmentation in street scenes"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bridging category level and instance level semantic image segmentation"
    - "identity mappings in deep residual networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)