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: "relaxed multiple instance svm with application to object discovery"
  Abstract: Multiple-instance learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking. In this paper, we propose a novel method to solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM). We treat the positiveness of instance as a continuous variable, use Noisy-OR model to enforce the MIL constraints, and jointly optimize the bag label and instance label in a unified framework. The optimization problem can be efficiently solved using stochastic gradient decent. The extensive experiments demonstrate that RMI-SVM consistently achieves superior performance on various benchmarks for MIL. Moreover, we simply applied RMI-SVM to a challenging vision task, common object discovery. The state-of-the-art results of object discovery on Pascal VOC datasets further confirm the advantages of the proposed method.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multi scale discriminative region discovery for weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self produced guidance for weakly supervised object localization"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "receptive field block net for accurate and fast object detection"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal action localization in a weakly supervised setting"):
  with members:
    - "predicting action tubes"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "incremental tube construction for human action detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "object detection from video tubelets with convolutional neural networks"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "towards weakly supervised action localization"
    - "automatic action annotation in weakly labeled videos"

Connected through hyperedge HE-4 (induced by source paper "pcl proposal cluster learning for weakly supervised object detection"):
  with members:
    - "self produced guidance for weakly supervised object localization"
    - "ts2c tight box mining with surrounding segmentation context 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"
    - "bayesian joint modelling for object localisation in weakly labelled images"
    - "weakly supervised object localization using things and stuff transfer"
    - "single shot object detection with enriched semantics"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-5 (induced by source paper "deep multiple instance learning for zero shot image tagging"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "deep patch learning for weakly supervised object classification and discovery"

Connected through hyperedge HE-6 (induced by source paper "learning from web data the benefit of unsupervised object localization"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "cascade r cnn delving into high quality object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "exploiting web images for dataset construction a domain robust approach"
    - "snn stacked neural networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "yolo9000 better faster stronger"
    - "learning to segment object candidates"

Connected through hyperedge HE-7 (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-8 (induced by source paper "deep multiple instance feature learning via variational autoencoder"):
  with members:
    - "multi instance learning by treating instances as non i i d samples"

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)