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: "r cnn minus r"
  Abstract: Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in order to determine whether it is a necessary modelling component, carrying essential geometric information not contained in the CNN, or whether it is merely a way of accelerating detection. We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating several learning steps in a single algorithm, as well as by proposing a number of improvements that accelerate detection.

Connected through hyperedge HE-1 (induced by source paper "neunets an automated synthesis engine for neural network design"):
  with members:
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "practical deep learning architecture optimization"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"

Connected through hyperedge HE-2 (induced by source paper "simultaneous region localization and hash coding for fine grained image retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "deep policy hashing network with listwise supervision"
    - "deep triplet quantization"
    - "feature pyramid hashing"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-3 (induced by source paper "learning orientation estimation convolutional neural network for building detection in optical remote sensing image"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "mask r cnn"
    - "a survey on object detection in optical remote sensing images"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "ssd single shot multibox detector"
    - "rapid building detection using machine learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "pneumothorax segmentation deep learning image segmentation to predict pneumothorax"):
  with members:
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "knowledge projection for deep neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "yolo9000 better faster stronger"
    - "deep fishernet for object classification"
    - "all you need is a good init"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-6 (induced by source paper "attentional network for visual object detection"):
  with members:
    - "unsupervised network pretraining via encoding human design"
    - "end to end memory networks"
    - "instance aware semantic segmentation via multi task network cascades"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-8 (induced by source paper "factors in finetuning deep model for object detection"):
  with members:
    - "object detection from video tubelets with convolutional neural networks"
    - "structured feature learning for pose estimation"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "fast r cnn"
    - "towards good practices for very deep two stream convnets"
    - "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)