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: "co localization with category consistent features and geodesic distance propagation"
  Abstract: Co-localization is the problem of localizing objects of the same class using only the set of images that contain them. This is a challenging task because the object detector must be built without negative examples that can lead to more informative supervision signals. The main idea of our method is to cluster the feature space of a generically pre-trained CNN, to find a set of CNN features that are consistently and highly activated for an object category, which we call category-consistent CNN features. Then, we propagate their combined activation map using superpixel geodesic distances for co-localization. In our first set of experiments, we show that the proposed method achieves state-of-the-art performance on three related benchmarks: PASCAL 2007, PASCAL-2012, and the Object Discovery dataset. We also show that our method is able to detect and localize truly unseen categories, on six held-out ImageNet categories with accuracy that is significantly higher than previous state-of-the-art. Our intuitive approach achieves this success without any region proposals or object detectors, and can be based on a CNN that was pre-trained purely on image classification tasks without further fine-tuning.

Connected through hyperedge HE-1 (induced by source paper "shadow removal via shadow image decomposition"):
  with members:
    - "weakly labeling the antarctic the penguin colony case"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "progressive growing of gans for improved quality stability and variation"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "interactive removal and ground truth for difficult shadow scenes"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (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"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "geodesic distance histogram feature for video segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep feature factorization for concept discovery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "why should i trust you explaining the predictions of any classifier"
    - "do semantic parts emerge in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)