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: "towards human machine cooperation self supervised sample mining for object detection"
  Abstract: Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision. To tackle this problem, many Active Learning (AL) methods have been developed. However, these methods mainly define their sample selection criteria within a single image context, leading to the suboptimal robustness and impractical solution for large-scale object detection. In this paper, aiming to remedy the drawbacks of existing AL methods, we present a principled Self-supervised Sample Mining (SSM) process accounting for the real challenges in object detection. Specifically, our SSM process concentrates on automatically discovering and pseudo-labeling reliable region proposals for enhancing the object detector via the introduced cross image validation, i.e., pasting these proposals into different labeled images to comprehensively measure their values under different image contexts. By resorting to the SSM process, we propose a new AL framework for gradually incorporating unlabeled or partially labeled data into the model learning while minimizing the annotating effort of users. Extensive experiments on two public benchmarks clearly demonstrate our proposed framework can achieve the comparable performance to the state-of-the-art methods with significantly fewer annotations.

Connected through hyperedge HE-1 (induced by source paper "deep active learning for remote sensing object detection"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "yolov3 an incremental improvement"
    - "cost effective object detection active sample mining with switchable selection criteria"
    - "robust face detection via learning small faces on hard images"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "learning roi transformer for detecting oriented objects in aerial images"
    - "active learning for cost sensitive classification"

Connected through hyperedge HE-2 (induced by source paper "learning loss for active learning"):
  with members:
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "active self paced learning for cost effective and progressive face identification"
    - "exploring the limits of weakly supervised pretraining"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "representation learning by learning to count"
    - "deep bayesian active learning with image data"
    - "stacked hourglass networks for human pose estimation"
    - "active and continuous exploration with deep neural networks and expected model output changes"

Connected through hyperedge HE-3 (induced by source paper "effortless deep training for traffic sign detection using templates and arbitrary natural images"):
  with members:
    - "self driving cars a survey"
    - "self paced deep learning for weakly supervised object detection"
    - "generative one shot learning gol a semi parametric approach to one shot learning in autonomous vision"
    - "few shot object detection via feature reweighting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstd a low shot transfer detector for object detection"
    - "deep learning based large scale automatic satellite crosswalk classification"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"

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)