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: "c mil continuation multiple instance learning for weakly supervised object detection"
  Abstract: Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multiple instance learning (MIL) and have non-convex loss functions which are prone to get stuck into local minima (falsely localize object parts) while missing full object extent during training. In this paper, we introduce a continuation optimization method into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way. We partition instances into spatially related and class related subsets, and approximate the original loss function with a series of smoothed loss functions defined within the subsets. Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions (SSERs) which indicate full object extent. On the PASCAL VOC 2007 and 2012 datasets, C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins.

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"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-2 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"
    - "mask r cnn"

Connected through hyperedge HE-4 (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"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "dissimilarity coefficient based weakly 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-5 (induced by source paper "object instance mining for weakly supervised object detection"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "dissimilarity coefficient based 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-6 (induced by source paper "attention on attention for image captioning"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "less is more picking informative frames for video captioning"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "exploring visual relationship for image captioning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "multiple anchor learning for visual object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "region proposal by guided anchoring"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "improving object detection with inverted attention"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised object detection with segmentation collaboration"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "group normalization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "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"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "cascaded pyramid network for multi person pose estimation"

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)