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: "compositional model based fisher vector coding for image classification"
  Abstract: Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to depict the generation process of local features. However, the representative power of the GMM could be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes and the number of prototypes is usually small in FVC. To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions. Instead, we adopt a compositional mechanism which assumes that a local feature is drawn from a Gaussian distribution whose mean vector is composed as the linear combination of multiple key components and the combination weight is a latent random variable. In this way, we can greatly enhance the representative power of the generative model of FVC. To implement our idea, we designed two particular generative models with such a compositional mechanism.

Connected through hyperedge HE-1 (induced by source paper "multi label image recognition with graph convolutional networks"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "chest x rays classification a multi label and fine grained problem"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"

Connected through hyperedge HE-2 (induced by source paper "scene recognition with prototype agnostic scene layout"):
  with members:
    - "semantic fisher scores for task transfer using objects to classify scenes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "graph r cnn for scene graph generation"
    - "a deeper look at power normalizations"
    - "situation recognition with graph neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "label embedding for image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "adaptive importance learning for improving lightweight image super resolution network"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "focal loss for dense object detection"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "accurate image super resolution using very deep convolutional 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)