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: "geodesic distance histogram feature for video segmentation"
  Abstract: This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets.

Connected through hyperedge HE-1 (induced by source paper "weakly labeling the antarctic the penguin colony case"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "deep extreme cut from extreme points to object segmentation"
    - "co localization with category consistent features and geodesic distance propagation"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "co localization with category consistent features and geodesic distance propagation"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "relaxed multiple instance svm with application to object discovery"

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)