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: "detector discovery in the wild joint multiple instance and representation learning"
  Abstract: We develop methods for detector learning which exploit joint training over both weak and strong labels and which transfer learned perceptual representations from strongly-labeled auxiliary tasks. Previous methods for weak-label learning often learn detector models independently using latent variable optimization, but fail to share deep representation knowledge across classes and usually require strong initialization. Other previous methods transfer deep representations from domains with strong labels to those with only weak labels, but do not optimize over individual latent boxes, and thus may miss specific salient structures for a particular category. We propose a model that subsumes these previous approaches, and simultaneously trains a representation and detectors for categories with either weak or strong labels present. We provide a novel formulation of a joint multiple instance learning method that includes examples from classification-style data when available, and also performs domain transfer learning to improve the underlying detector representation. Our model outperforms known methods on ImageNet-200 detection with weak labels.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised multi person action recognition in 360 circ videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised learning of view invariant action representations"
    - "improved person detection on omnidirectional images with non maxima suppression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual glance model for deciphering social relationships"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-2 (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-3 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "label refinery improving imagenet classification through label progression"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-5 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-6 (induced by source paper "towards precise end to end weakly supervised object detection network"):
  with members:
    - "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"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "c wsl count guided weakly supervised localization"
    - "attention is all you need"
    - "deep self taught learning for weakly supervised object localization"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "learning to find common objects across few image collections"):
  with members:
    - "attention based deep multiple instance learning"
    - "prototypical networks for few shot learning"
    - "deep self taught learning for weakly supervised object localization"
    - "revisiting knowledge transfer for training object class detectors"
    - "few shot image recognition by predicting parameters from activations"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "rapid adaptation with conditionally shifted neurons"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-8 (induced by source paper "self paced deep learning for weakly supervised object detection"):
  with members:
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "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"

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)