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: "self paced deep learning for weakly supervised object detection"
  Abstract: In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level ground truth is not available, most of the solutions proposed so far are based on an iterative, Multiple Instance Learning framework in which the current classifier is used to select the highest-confidence boxes in each image, which are treated as pseudo-ground truth in the next training iteration. However, the errors of an immature classifier can make the process drift, usually introducing many of false positives in the training dataset. To alleviate this problem, we propose in this paper a training protocol based on the self-paced learning paradigm. The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training. While in the past few years similar strategies have been adopted for SVMs and other classifiers, we are the first showing that a self-paced approach can be used with deep-network-based classifiers in an end-to-end training pipeline. The method we propose is built on the fully-supervised Fast-RCNN architecture and can be applied to similar architectures which represent the input image as a bag of boxes. We show state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013. On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform even those weakly-supervised approaches which are based on much higher-capacity networks.

Connected through hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "confidence regularized self training"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "unsupervised domain adaptation through self supervision"

Connected through hyperedge HE-2 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "low shot learning from imaginary 3d model"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "experience replay for continual learning"
    - "hierarchy of gans for learning embodied self awareness model"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "foil it find one mismatch between image and language caption"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"

Connected through hyperedge HE-4 (induced by source paper "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"):
  with members:
    - "pseudo labeling curriculum for unsupervised domain adaptation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "zigzag learning for weakly supervised object detection"
    - "open set domain adaptation by backpropagation"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "strong weak distribution alignment for adaptive object detection"

Connected through hyperedge HE-5 (induced by source paper "low shot learning from imaginary 3d model"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "low shot learning from imaginary data"
    - "low shot learning with large scale diffusion"
    - "prototypical networks for few shot learning"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "learning feed forward one shot learners"
    - "3d menagerie modeling the 3d shape and pose of animals"

Connected through hyperedge HE-6 (induced by source paper "effortless deep training for traffic sign detection using templates and arbitrary natural images"):
  with members:
    - "self driving cars a survey"
    - "generative one shot learning gol a semi parametric approach to one shot learning in autonomous vision"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "self paced multi label learning with diversity"):
  with members:
    - "active self paced learning for cost effective and progressive face identification"
    - "multi label learning with global and local label correlation"
    - "self paced multi task learning"
    - "a unified view of multi label performance measures"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-8 (induced by source paper "self paced probabilistic principal component analysis for data with outliers"):
  with members:
    - "online robust principal component analysis with change point detection"
    - "efficient l1 norm principal component analysis via bit flipping"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "what objective does self paced learning indeed optimize"
    - "robust principal component analysis"

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)