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: "progressive attention networks for visual attribute prediction"
  Abstract: We propose a novel attention model that can accurately attends to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process over multiple layers of a convolutional neural network. The attentive process in each layer determines whether to pass or block features at certain spatial locations for use in the subsequent layers. The proposed progressive attention mechanism works well especially when combined with hard attention. We further employ local contexts to incorporate neighborhood features of each location and estimate a better attention probability map. The experiments on synthetic and real datasets show that the proposed attention networks outperform traditional attention methods in visual attribute prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "res3atn deep 3d residual attention network for hand gesture recognition in videos"):
  with members:
    - "real time hand gesture detection and classification using convolutional neural networks"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "residual attention network for image classification"
    - "hierarchical attention networks"

Connected through hyperedge HE-2 (induced by source paper "attentionrnn a structured spatial attention mechanism"):
  with members:
    - "modular generative adversarial networks"
    - "explainable neural computation via stack neural module networks"
    - "cbam convolutional block attention module"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "visual reference resolution using attention memory for visual dialog"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-3 (induced by source paper "conditionally learn to pay attention for sequential visual task"):
  with members:
    - "learn to pay attention"
    - "a unified query based generative model for question generation and question answering"
    - "diversity driven attention model for query based abstractive summarization"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "hierarchical attention networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "multiple instance dense connected convolution neural network for aerial image scene classification"):
  with members:
    - "attention based deep multiple instance learning"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "hierarchical attention networks"
    - "densely connected convolutional networks"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (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"
    - "stnet selective tuning of convolutional networks for object localization"
    - "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-6 (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"
    - "stnet selective tuning of convolutional networks for object localization"
    - "residual attention network for image classification"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-7 (induced by source paper "feature prioritization and regularization improve standard accuracy and adversarial robustness"):
  with members:
    - "training verified learners with learned verifiers"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "built in vulnerabilities to imperceptible adversarial perturbations"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "improving the generalization of adversarial training with domain adaptation"
    - "learn to pay attention"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)