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: "weakly and semi supervised object detection with expectation maximization algorithm"
  Abstract: Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an Expectation-Maximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semi-supervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN. We share our source code at this https URL

Connected through hyperedge HE-1 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement"):
  with members:
    - "baod budget aware object detection"
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "region proposal by guided anchoring"
    - "revisiting feature alignment for one stage object detection"
    - "min entropy latent model for weakly supervised object detection"
    - "feature selective anchor free module for single shot object detection"
    - "c mil continuation multiple instance learning for weakly supervised object detection"

Connected through hyperedge HE-2 (induced by source paper "weakly and semi supervised detection in medical imaging via deep dual branch net"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "optimizing region selection for weakly supervised object detection"
    - "who said what modeling individual labelers improves classification"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"

Connected through hyperedge HE-3 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-5 (induced by source paper "a dual branch deep neural network for classification and detection in mammograms"):
  with members:
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "optimizing region selection for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "weakly supervised deep detection networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (induced by source paper "dissimilarity coefficient based weakly supervised object detection"):
  with members:
    - "zigzag learning for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "learning human poses from actions"
    - "collaborative learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "saliency guided end to end learning for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-7 (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"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "automatic subspace learning via principal coefficients embedding"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "sampling techniques for large scale object detection from sparsely annotated objects"):
  with members:
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft sampling for robust object detection"
    - "solution for large scale hierarchical object detection datasets with incomplete annotation and data imbalance"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "squeeze and excitation networks"

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)