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: "dissimilarity coefficient based weakly supervised object detection"
  Abstract: We consider the problem of weakly supervised object detection, where the training samples are annotated using only image-level labels that indicate the presence or absence of an object category. In order to model the uncertainty in the location of the objects, we employ a dissimilarity coefficient based probabilistic learning objective. The learning objective minimizes the difference between an annotation agnostic prediction distribution and an annotation aware conditional distribution. The main computational challenge is the complex nature of the conditional distribution, which consists of terms over hundreds or thousands of variables. The complexity of the conditional distribution rules out the possibility of explicitly modeling it. Instead, we exploit the fact that deep learning frameworks rely on stochastic optimization. This allows us to use a state of the art discrete generative model that can provide annotation consistent samples from the conditional distribution. Extensive experiments on PASCAL VOC 2007 and 2012 data sets demonstrate the efficacy of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "in defense of graph inference algorithms for weakly supervised object localization"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "learning to find common objects across few image collections"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "sniper efficient multi scale training"
    - "focal loss for dense object detection"
    - "revisiting knowledge transfer for training object class detectors"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "object instance mining for weakly supervised object detection"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "c wsl count guided weakly supervised localization"
    - "deep self taught learning for weakly supervised object localization"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-3 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

Connected through hyperedge HE-4 (induced by source paper "learning to relate from captions and bounding boxes"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "zigzag learning for weakly supervised object detection"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "improved image captioning via policy gradient optimization of spider"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

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)