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 cross season correspondence dataset for robust semantic segmentation"
  Abstract: In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.

Connected through hyperedge HE-1 (induced by source paper "fine grained segmentation networks self supervised segmentation for improved long term visual localization"):
  with members:
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep clustering for unsupervised learning of visual features"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "unsupervised representation learning by predicting image rotations"
    - "vlase vehicle localization by aggregating semantic edges"

Connected through hyperedge HE-2 (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"
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "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"

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)