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: "object localization in imagenet by looking out of the window"
  Abstract: We propose a method for annotating the location of objects in ImageNet. Traditionally, this is cast as an image window classification problem, where each window is considered independently and scored based on its appearance alone. Instead, we propose a method which scores each candidate window in the context of all other windows in the image, taking into account their similarity in appearance space as well as their spatial relations in the image plane. We devise a fast and exact procedure to optimize our scoring function over all candidate windows in an image, and we learn its parameters using structured output regression. We demonstrate on 92000 images from ImageNet that this significantly improves localization over recent techniques that score windows in isolation.

Connected through hyperedge HE-1 (induced by source paper "learning non maximum suppression"):
  with members:
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "learning convolutional neural networks for graphs"
    - "end to end training of object class detectors for mean average precision"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "a convnet for non maximum suppression"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "zero shot object prediction and context modeling using semantic scene knowledge"):
  with members:
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "building a large scale multimodal knowledge base system for answering visual queries"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-3 (induced by source paper "hi detector what s wrong with that object identifying irregular object from images by modelling the detection score distribution"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "associative embeddings for large scale knowledge transfer with self assessment"

Connected through hyperedge HE-4 (induced by source paper "no spare parts sharing part detectors for image categorization"):
  with members:
    - "mid level deep pattern mining"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "spotlight the negatives a generalized discriminative latent model"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-5 (induced by source paper "a convnet for non maximum suppression"):
  with members:
    - "what makes for effective detection proposals"
    - "learning to segment object candidates"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "end to end people detection in crowded scenes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "towards open set deep networks"):
  with members:
    - "understanding neural networks through deep visualization"
    - "towards open world recognition"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "exploring person context and local scene context for object detection"):
  with members:
    - "contextual action recognition with r cnn"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "fast r cnn"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "scalable high quality object detection"
    - "microsoft coco common objects in context"

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)