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: "deep learning for single view instance recognition"
  Abstract: Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small number of images available for each product. We explore the use of deep learning methods for recognizing object instances when we have only a single training example per class. We show that feedforward neural networks outperform state-of-the-art methods for recognizing objects from novel viewpoints even when trained from just a single image per object. To further improve our performance on this task, we propose to take advantage of a supplementary dataset in which we observe a separate set of objects from multiple viewpoints. We introduce a new approach for training deep learning methods for instance recognition with limited training data, in which we use an auxiliary multi-view dataset to train our network to be robust to viewpoint changes. We find that this approach leads to a more robust classifier for recognizing objects from novel viewpoints, outperforming previous state-of-the-art approaches including keypoint-matching, template-based techniques, and sparse coding.

Connected through hyperedge HE-1 (induced by source paper "detecting retail products in situ using cnn without human effort labeling"):
  with members:
    - "fssd feature fusion single shot multibox detector"
    - "dssd deconvolutional single shot detector"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"
    - "the freiburg groceries dataset"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "self supervisory signals for object discovery and detection"):
  with members:
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "object category learning and retrieval with weak supervision"
    - "a dataset for developing and benchmarking active vision"
    - "time contrastive networks self supervised learning from video"
    - "curiosity driven exploration by self supervised prediction"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-3 (induced by source paper "complex event recognition from images with few training examples"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "automatic concept discovery from parallel text and visual corpora"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "building a large concept bank for representing events in video"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "conceptlearner discovering visual concepts from weakly labeled image collections"

Connected through hyperedge HE-4 (induced by source paper "multiview rgb d dataset for object instance detection"):
  with members:
    - "rgbd datasets past present and future"
    - "ssd single shot multibox detector"
    - "learning to segment object candidates"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "scalable high quality object detection"
    - "microsoft coco common objects in context"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)