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: "attentive contexts for object detection"
  Abstract: Modern deep neural network based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. In this work, we take a step towards understanding what is a robust practice to extract and utilize contextual information to facilitate object detection in practice. Specifically, we consider the following two questions: "how to identify useful global contextual information for detecting a certain object?" and "how to exploit local context surrounding a proposal for better inferring its contents?". We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model. AC-CNN effectively incorporates global and local contextual information into the region-based CNN (e.g. Fast RCNN) detection model and provides better object detection performance. It consists of one attention-based global contextualized (AGC) sub-network and one multi-scale local contextualized (MLC) sub-network. To capture global context, the AGC sub-network recurrently generates an attention map for an input image to highlight useful global contextual locations, through multiple stacked Long Short-Term Memory (LSTM) layers. For capturing surrounding local context, the MLC sub-network exploits both the inside and outside contextual information of each specific proposal at multiple scales. The global and local context are then fused together for making the final decision for detection. Extensive experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of the proposed AC-CNN over well-established baselines. In particular, AC-CNN outperforms the popular Fast-RCNN by 2.0% and 2.2% on VOC 2007 and VOC 2012 in terms of mAP, respectively.

Connected through hyperedge HE-1 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "attribute aware pedestrian detection in a crowd"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "acquisition of localization confidence for accurate object detection"
    - "iterative crowd counting"

Connected through hyperedge HE-3 (induced by source paper "spatial aware non local attention for fashion landmark detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"
    - "non local neural networks"
    - "pose guided person image generation"
    - "attention is all you need"
    - "creating capsule wardrobes from fashion images"
    - "deformable gans for pose based human image generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"

Connected through hyperedge HE-4 (induced by source paper "graphonomy universal human parsing via graph transfer learning"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"

Connected through hyperedge HE-5 (induced by source paper "assd attentive single shot multibox detector"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "learn to pay attention"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "relation networks for object detection"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-6 (induced by source paper "feature intertwiner for object detection"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "neural network encapsulation"
    - "gradient harmonized single stage detector"
    - "grid r cnn"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "improving gans using optimal transport"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-7 (induced by source paper "repgn object detection with relational proposal graph network"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "sequential context encoding for duplicate removal"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "path aggregation network for instance segmentation"
    - "relation networks for object detection"
    - "yes net an effective detector based on global information"
    - "cascade r cnn delving into high quality object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "widerperson a diverse dataset for dense pedestrian detection in the wild"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

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)