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: "modelling local deep convolutional neural network features to improve fine grained image classification"
  Abstract: We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition. However, to date there has been limited work using these deep CNNs as local feature extractors. This partly stems from CNNs having internal representations which are high dimensional, thereby making such representations difficult to model using stochastic models. To overcome this issue, we propose to reduce the dimensionality of one of the internal fully connected layers, in conjunction with layer-restricted retraining to avoid retraining the entire network. The distribution of low-dimensional features obtained from the modified layer is then modelled using a Gaussian mixture model. Comparative experiments show that considerable performance improvements can be achieved on the challenging Fish and UEC FOOD-100 datasets.

Connected through hyperedge HE-1 (induced by source paper "aquasight automatic water impurity detection utilizing convolutional neural networks"):
  with members:
    - "algae detection using computer vision and deep learning"

Connected through hyperedge HE-2 (induced by source paper "discovering and generating hard examples for training a red tide detector"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "cross domain cnn for hyperspectral image classification"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "future frame prediction for anomaly detection a new baseline"
    - "residual conv deconv grid network for semantic segmentation"
    - "contextual deep cnn based hyperspectral classification"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised feature learning toward a real time vehicle make and model recognition"):
  with members:
    - "convolutional low resolution fine grained classification"
    - "fine grained classification via mixture of deep convolutional neural networks"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-4 (induced by source paper "an open source c implementation of multi threaded gaussian mixture models k means and expectation maximisation"):
  with members:
    - "exploiting temporal information for dcnn based fine grained object classification"
    - "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions"
    - "on robust face recognition via sparse coding the good the bad and the ugly"
    - "improved foreground detection via block based classifier cascade with probabilistic decision integration"
    - "mlpack a scalable c machine learning library"
    - "revisiting k means new algorithms via bayesian nonparametrics"
    - "the pitfalls of verifying floating point computations"

Connected through hyperedge HE-5 (induced by source paper "exploiting temporal information for dcnn based fine grained object classification"):
  with members:
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "beyond short snippets deep networks for video classification"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "part based r cnns for fine grained category detection"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "simultaneous food localization and recognition"):
  with members:
    - "toward storytelling from visual lifelogging an overview"
    - "leveraging context to support automated food recognition in restaurants"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "subset feature learning for fine grained category classification"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "part based r cnns for fine grained category detection"
    - "bird species categorization using pose normalized deep convolutional nets"
    - "simultaneous detection and segmentation"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-8 (induced by source paper "fine grained classification via mixture of deep convolutional neural networks"):
  with members:
    - "going deeper with convolutions"
    - "part based r cnns for fine grained category detection"
    - "caffe convolutional architecture for fast feature embedding"

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)