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: "efficient segmentation learning downsampling near semantic boundaries"
  Abstract: Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Cost-performance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects.

Connected through hyperedge HE-1 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "pointrend image segmentation as rendering"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "high resolution representations for labeling pixels and regions"
    - "tensormask a foundation for dense object segmentation"
    - "panoptic feature pyramid networks"
    - "rethinking imagenet pre training"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "fasterseg searching for faster real time semantic segmentation"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "customizable architecture search for semantic segmentation"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "squeezenas fast neural architecture search for faster semantic segmentation"

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)