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: "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
  Abstract: Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

Connected through hyperedge HE-1 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"):
  with members:
    - "semantic segmentation of panoramic images using a synthetic dataset"
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "don t worry about the weather unsupervised condition dependent domain adaptation"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "i can see clearly now image restoration via de raining"
    - "a cross season correspondence dataset for robust semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a comparative study of high recall real time semantic segmentation based on swift factorized network"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "multi label image recognition with graph convolutional networks"
    - "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-3 (induced by source paper "semantic segmentation of panoramic images using a synthetic dataset"):
  with members:
    - "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"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "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"

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)