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: "zoom out and in network with recursive training for object proposal"
  Abstract: In this paper, we propose a zoom-out-and-in network for generating object proposals. We utilize different resolutions of feature maps in the network to detect object instances of various sizes. Specifically, we divide the anchor candidates into three clusters based on the scale size and place them on feature maps of distinct strides to detect small, medium and large objects, respectively. Deeper feature maps contain region-level semantics which can help shallow counterparts to identify small objects. Therefore we design a zoom-in sub-network to increase the resolution of high level features via a deconvolution operation. The high-level features with high resolution are then combined and merged with low-level features to detect objects. Furthermore, we devise a recursive training pipeline to consecutively regress region proposals at the training stage in order to match the iterative regression at the testing stage. We demonstrate the effectiveness of the proposed method on ILSVRC DET and MS COCO datasets, where our algorithm performs better than the state-of-the-arts in various evaluation metrics. It also increases average precision by around 2% in the detection system.

Connected through hyperedge HE-1 (induced by source paper "attentionmask attentive efficient object proposal generation focusing on small objects"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "ron reverse connection with objectness prior networks for object detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "mask r cnn"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "instance sensitive fully convolutional networks"
    - "image captioning with semantic attention"

Connected through hyperedge HE-2 (induced by source paper "learning feature pyramids for human pose estimation"):
  with members:
    - "towards accurate multi person pose estimation in the wild"
    - "pose invariant embedding for deep person re identification"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "recurrent scale approximation for object detection in cnn"):
  with members:
    - "learning deep features via congenerous cosine loss for person recognition"
    - "quality aware network for set to set recognition"
    - "scale aware face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "poi multiple object tracking with high performance detection and appearance feature"

Connected through hyperedge HE-4 (induced by source paper "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "person search with natural language description"
    - "object detection in videos with tubelet proposal networks"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "joint detection and identification feature learning for person search"
    - "a large scale car dataset for fine grained categorization and verification"
    - "car segmentation and pose estimation using 3d object models"
    - "learning to rank in person re identification with metric ensembles"

Connected through hyperedge HE-5 (induced by source paper "crafting gbd net for object detection"):
  with members:
    - "factors in finetuning deep model for object detection"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "structured feature learning for pose estimation"
    - "what makes for effective detection proposals"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "multi bias non linear activation in deep neural 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)