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: "recent advances in convolutional neural networks"
  Abstract: In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Due to the lack of training data and computing power in early days, it is hard to train a large high-capacity convolutional neural network without overfitting. After the rapid growth in the amount of the annotated data and the recent improvements in the strengths of graphics processor units (GPUs), the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. Besides, we also introduce some applications of convolutional neural networks in computer vision.

Connected through hyperedge HE-1 (induced by source paper "optimising the input image to improve visual relationship detection"):
  with members:
    - "referring relationships"
    - "detecting visual relationships with deep relational networks"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "mask r cnn"
    - "visual translation embedding network for visual relation detection"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "visual relationship detection with language priors"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-2 (induced by source paper "deep octonion networks"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "deep quaternion networks"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an overview of multi task learning in deep neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-3 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-4 (induced by source paper "neurostream scalable and energy efficient deep learning with smart memory cubes"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "a near threshold risc v core with dsp extensions for scalable iot endpoint devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "identity mappings in deep residual networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "fathom reference workloads for modern deep learning methods"
    - "a taxonomy of deep convolutional neural nets for computer vision"

Connected through hyperedge HE-5 (induced by source paper "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "context discovery for model learning in partially observable environments"
    - "autonomous grounding of visual field experience through sensorimotor prediction"
    - "the curious robot learning visual representations via physical interactions"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "learning image representations equivariant to ego motion"
    - "you only look once unified real time object detection"
    - "grounding object perception in a naive agent s sensorimotor experience"

Connected through hyperedge HE-6 (induced by source paper "a systematic study of the class imbalance problem in convolutional neural networks"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "the inaturalist species classification and detection dataset"
    - "imagenet pre trained models with batch normalization"
    - "detection of concealed cars in complex cargo x ray imagery using deep learning"
    - "relay backpropagation for effective learning of deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "a novel deep neural network architecture for mars visual navigation"):
  with members:
    - "deep reinforcement learning an overview"
    - "memory augmented control networks"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "value iteration networks"
    - "deep residual learning for image recognition"
    - "end to end training of deep visuomotor policies"
    - "neural network model for path planning of robotic rover systems"

Connected through hyperedge HE-8 (induced by source paper "handwriting recognition of historical documents with few labeled data"):
  with members:
    - "full page text recognition learning where to start and when to stop"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "adadelta an adaptive learning rate method"

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)