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: "stnet selective tuning of convolutional networks for object localization"
  Abstract: Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feedforward processing on the abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of Convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps.

Connected through hyperedge HE-1 (induced by source paper "selective segmentation networks using top down attention"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised object localization with inter intra regulated cams"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "randaugment practical data augmentation with no separate search"
    - "fixing the train test resolution discrepancy"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "intriguing properties of randomly weighted networks generalizing while learning next to nothing"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "network dissection quantifying interpretability of deep visual representations"
    - "incremental learning through deep adaptation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning discrete weights using the local reparameterization trick"

Connected through hyperedge HE-4 (induced by source paper "fpan fine grained and progressive attention localization network for data retrieval"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "dssd deconvolutional single shot detector"
    - "dual deep network for visual tracking"
    - "ensemble of part detectors for simultaneous classification and localization"
    - "progressive attention networks for visual attribute prediction"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "xception deep learning with depthwise separable convolutions"
    - "modeling and propagating cnns in a tree structure for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "learning what and where to attend"):
  with members:
    - "differential attention for visual question answering"
    - "learn to pay attention"
    - "smoothgrad removing noise by adding noise"
    - "interpreting deep visual representations via network dissection"
    - "what are the visual features underlying human versus machine vision"
    - "residual attention network for image classification"
    - "a neural representation of sketch drawings"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-6 (induced by source paper "priming neural networks"):
  with members:
    - "segmentation aware convolutional networks using local attention masks"
    - "modulating early visual processing by language"
    - "film visual reasoning with a general conditioning layer"
    - "object detection can be improved using human derived contextual expectations"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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)