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: "orientation aware semantic segmentation on icosahedron spheres"
  Abstract: We address semantic segmentation on omnidirectional images, to leverage a holistic understanding of the surrounding scene for applications like autonomous driving systems. For the spherical domain, several methods recently adopt an icosahedron mesh, but systems are typically rotation invariant or require significant memory and parameters, thus enabling execution only at very low resolutions. In our work, we propose an orientation-aware CNN framework for the icosahedron mesh. Our representation allows for fast network operations, as our design simplifies to standard network operations of classical CNNs, but under consideration of north-aligned kernel convolutions for features on the sphere. We implement our representation and demonstrate its memory efficiency up-to a level-8 resolution mesh (equivalent to 640 x 1024 equirectangular images). Finally, since our kernels operate on the tangent of the sphere, standard feature weights, pretrained on perspective data, can be directly transferred with only small need for weight refinement. In our evaluation our orientation-aware CNN becomes a new state of the art for the recent 2D3DS dataset, and our Omni-SYNTHIA version of SYNTHIA. Rotation invariant classification and segmentation tasks are additionally presented for comparison to prior art.

Connected through hyperedge HE-1 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "cross view semantic segmentation for sensing surroundings"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "weakly supervised silhouette based semantic change detection"

Connected through hyperedge HE-2 (induced by source paper "semantic segmentation of panoramic images using a synthetic dataset"):
  with members:
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from panoramic video"
    - "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems"
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"

Connected through hyperedge HE-3 (induced by source paper "tangent images for mitigating spherical distortion"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "mapped convolutions"
    - "convolutions on spherical images"
    - "kernel transformer networks for compact spherical convolution"
    - "learning so 3 equivariant representations with spherical cnns"
    - "convolutional networks for spherical signals"
    - "joint 2d 3d semantic data for indoor scene understanding"

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)