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: "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
  Abstract: Weakly-supervised object detection (WOD) is a challenging problems in computer vision. The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors, given only the training images with weak image-level labels. Intuitively, by simulating the selective attention mechanism of human visual system, saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD. However, the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be possibly highly ambiguous in complex cases. To this end, this paper first comprehensively analyzes the challenges in applying saliency detection to WOD. Then, we make one of the earliest efforts to bridge saliency detection to WOD via the self-paced curriculum learning, which can guide the learning procedure to gradually achieve faithful knowledge of multi-class objects from easy to hard. The experimental results demonstrate that the proposed approach can successfully bridge saliency detection and WOD tasks and achieve the state-of-the-art object detection results under the weak supervision.

Connected through hyperedge HE-1 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "learning to segment human by watching youtube"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-2 (induced by source paper "self paced deep learning for weakly supervised object detection"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised object localization using size estimates"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "webly supervised learning of convolutional networks"
    - "contextual action recognition with r cnn"
    - "weakly supervised deep detection networks"

Connected through hyperedge HE-3 (induced by source paper "low shot learning from imaginary 3d model"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "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"
    - "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-4 (induced by source paper "self paced probabilistic principal component analysis for data with outliers"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "online robust principal component analysis with change point detection"
    - "efficient l1 norm principal component analysis via bit flipping"
    - "what objective does self paced learning indeed optimize"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "self paced adversarial training for multimodal few shot learning"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "chatpainter improving text to image generation using dialogue"
    - "low shot learning from imaginary data"
    - "low shot learning with large scale diffusion"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-6 (induced by source paper "cost effective object detection active sample mining with switchable selection criteria"):
  with members:
    - "few example object detection with model communication"
    - "connections between nuclear norm and frobenius norm based representations"
    - "active self paced learning for cost effective and progressive face identification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "cost effective active learning for deep image classification"
    - "automatic subspace learning via principal coefficients embedding"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "one click annotation with guided hierarchical object detection"):
  with members:
    - "few shot learning with metric agnostic conditional embeddings"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "annotating object instances with a polygon rnn"
    - "learning intelligent dialogs for bounding box annotation"
    - "weakly and semi supervised object detection with expectation maximization algorithm"

Connected through hyperedge HE-8 (induced by source paper "zero shot learning via latent space encoding"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "transductive zero shot learning with a self training dictionary approach"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "transductive zero shot learning with adaptive structural embedding"
    - "learning a deep embedding model for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view zero shot learning"

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)