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: "deepid net multi stage and deformable deep convolutional neural networks for object detection"
  Abstract: In this paper, we propose multi-stage and deformable deep convolutional neural networks for object detection. This new deep learning object detection diagram has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (defpooling) layer models the deformation of object parts with geometric constraint and penalty. With the proposed multistage training strategy, multiple classifiers are jointly optimized to process samples at different difficulty levels. A ne w pre-training strategy is proposed to learn feature represe ntations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of modeling averaging. The proposed approach ranked #2 in ILSVRC 2014. It improves the mean averaged precision obtained by RCNN, which is the stateof-the-art of object detection, from31% to 45%. Detailed component-wise analysis is also provided through extensiv e experimental evaluation.

Connected through hyperedge HE-1 (induced by source paper "relation distillation networks for video object detection"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "videos as space time region graphs"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "exploring visual relationship for image captioning"
    - "non local neural networks"
    - "towards high performance video object detection"

Connected through hyperedge HE-2 (induced by source paper "zero shot crowd behavior recognition"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "transductive multi label zero shot learning"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "zero shot learning through cross modal transfer"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "ct image super resolution using 3d convolutional neural network"):
  with members:
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "probabilistic model of object detection based on convolutional neural network"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "deepid net deformable deep convolutional neural networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "hybrid cnn and dictionary based models for scene recognition and domain adaptation"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "places205 vggnet models for scene recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "action driven object detection with top down visual attentions"):
  with members:
    - "ssd single shot multibox detector"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "spatial transformer networks"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "learning contextual dependence with convolutional hierarchical recurrent neural networks"):
  with members:
    - "beyond short snippets deep networks for video classification"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "draw a recurrent neural network for image generation"
    - "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"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "recurrent models of visual attention"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised fine grained categorization with part based image representation"):
  with members:
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "fast r cnn"
    - "r cnn minus r"
    - "a comparison of dense region detectors for image search and fine grained classification"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fine grained recognition datasets for biodiversity analysis"
    - "very deep convolutional networks for large scale image recognition"

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)