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: "attention branch network learning of attention mechanism for visual explanation"
  Abstract: Visual explanation enables human to understand the decision making of Deep Convolutional Neural Network (CNN), but it is insufficient to contribute the performance improvement. In this paper, we focus on the attention map for visual explanation, which represents high response value as the important region in image recognition. This region significantly improves the performance of CNN by introducing an attention mechanism that focuses on a specific region in an image. In this work, we propose Attention Branch Network (ABN), which extends the top-down visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for attention mechanism and is trainable for the visual explanation and image recognition in end-to-end manner. We evaluate ABN on several image recognition tasks such as image classification, fine-grained recognition, and multiple facial attributes recognition. Experimental results show that ABN can outperform the accuracy of baseline models on these image recognition tasks while generating an attention map for visual explanation. Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "localizing multi scale semantic patches for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "saccader improving accuracy of hard attention models for vision"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "searching for mobilenetv3"
    - "gated convolutional networks with hybrid connectivity for image classification"
    - "cbam convolutional block attention module"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-3 (induced by source paper "emoticon context aware multimodal emotion recognition using frege s principle"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "identifying emotions from walking using affective and deep features"
    - "group emotion recognition using machine learning"
    - "image captioning with integrated bottom up and multi level residual top down attention for game scene understanding"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "saccader improving accuracy of hard attention models for vision"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "attention is not explanation"
    - "do better imagenet models transfer better"
    - "feature denoising for improving adversarial robustness"
    - "regularized evolution for image classifier architecture search"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-5 (induced by source paper "embedding human knowledge in deep neural network via attention map"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "methods for interpreting and understanding deep neural networks"
    - "rise randomized input sampling for explanation of black box models"
    - "smoothgrad removing noise by adding noise"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "deep features analysis with attention networks"
    - "how do convolutional neural networks learn design"
    - "innvestigate neural networks"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "learn to segment retinal lesions and beyond"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "weakly supervised instance segmentation using class peak response"
    - "learning a discriminative feature network for semantic segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "ccnet criss cross attention for semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"

Connected through hyperedge HE-8 (induced by source paper "dual attention focused module for weakly supervised object localization"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "self produced guidance for weakly supervised object localization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "tell me where to look guided attention inference network"

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)