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: "recurrent spatial transformer networks"
  Abstract: We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance. The down-sampling in RNN-SPN can be thought of as adaptive down-sampling that minimizes the information loss in the regions of interest. We attribute the superior performance of the RNN-SPN to the fact that it can attend to a sequence of regions of interest.

Connected through hyperedge HE-1 (induced by source paper "learning for video compression"):
  with members:
    - "learning based facial image compression with semantic fidelity metric"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "an end to end compression framework based on convolutional neural networks"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "neural network based arithmetic coding of intra prediction modes in hevc"
    - "learning to inpaint for image compression"

Connected through hyperedge HE-2 (induced by source paper "video person re identification by temporal residual learning"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "person re identification by camera correlation aware feature augmentation"
    - "person re identification via recurrent feature aggregation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"

Connected through hyperedge HE-3 (induced by source paper "stampnet unsupervised multi class object discovery"):
  with members:
    - "yolov3 an incremental improvement"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "dynamic capacity networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a biologically inspired visual working memory for deep networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "torchbearer a model fitting library for pytorch"
    - "dynamic routing between capsules"
    - "enriched deep recurrent visual attention model for multiple object recognition"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "spatial transformer networks"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-5 (induced by source paper "magicvo end to end monocular visual odometry through deep bi directional recurrent convolutional neural network"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "enriched deep recurrent visual attention model for multiple object recognition"):
  with members:
    - "recurrent batch normalization"
    - "learning both weights and connections for efficient neural networks"
    - "blocks and fuel frameworks for deep learning"
    - "dynamic capacity networks"
    - "spatial transformer networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "see towards semi supervised end to end scene text recognition"):
  with members:
    - "visualbackprop efficient visualization of cnns"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "end to end interpretation of the french street name signs dataset"
    - "attention based extraction of structured information from street view imagery"
    - "synthetic data for text localisation in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "instance aware semantic segmentation via multi task network cascades"
    - "spatial transformer networks"

Connected through hyperedge HE-8 (induced by source paper "stn ocr a single neural network for text detection and text recognition"):
  with members:
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "synthetic data for text localisation in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "spatial transformer networks"
    - "reading scene text in deep convolutional sequences"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal 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)