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: "a survey of semantic segmentation"
  Abstract: This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. Recently published approaches with convolutional neural networks are mentioned and typical problematic situations for segmentation algorithms are examined. A taxonomy of segmentation algorithms is given.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based architectures for semantic segmentation on 2d images"):
  with members:
    - "panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "yolact real time instance segmentation"
    - "convolutional crfs for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic video segmentation a review on recent approaches"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-2 (induced by source paper "waterfall atrous spatial pooling architecture for efficient semantic segmentation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"

Connected through hyperedge HE-4 (induced by source paper "recent progress in semantic image segmentation"):
  with members:
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "dilated residual networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "real time facial segmentation and performance capture from rgb input"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "a methodological review of visual road recognition procedures for autonomous driving applications"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning a driving simulator"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-6 (induced by source paper "dynamic multicontext segmentation of remote sensing images based on convolutional networks"):
  with members:
    - "exploiting convnet diversity for flooding identification"
    - "deep learning in remote sensing a review"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "classification with an edge improving semantic image segmentation with boundary detection"

Connected through hyperedge HE-7 (induced by source paper "openeds open eye dataset"):
  with members:
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"

Connected through hyperedge HE-8 (induced by source paper "deep dual pyramid network for barcode segmentation using barcode 30k database"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "region based semantic segmentation with end to end training"

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)