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: "encoding high dimensional local features by sparse coding based fisher vectors"
  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 characterize the generation process of local features. This choice has shown to be sufficient for traditional low dimensional local features, e.g., SIFT; and typically, good performance can be achieved with only a few hundred Gaussian distributions. However, the same number of Gaussians is insufficient to model the feature space spanned by higher dimensional local features, which have become popular recently. In order to improve the modeling capacity for high dimensional features, it turns out to be inefficient and computationally impractical to simply increase the number of Gaussians. In this paper, we propose a model in which each local feature is drawn from a Gaussian distribution whose mean vector is sampled from a subspace. With certain approximation, this model can be converted to a sparse coding procedure and the learning/inference problems can be readily solved by standard sparse coding methods. By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC). Moreover, we adopt the recently developed Deep Convolutional Neural Network (CNN) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC. Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition, indoor scene, and fine-grained image classification problems.

Connected through hyperedge HE-1 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization"):
  with members:
    - "cbam convolutional block attention module"
    - "is second order information helpful for large scale visual recognition"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "towards effective codebookless model for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep ten texture encoding network"

Connected through hyperedge HE-2 (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"
    - "compositional model based fisher vector coding for image classification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "local area transform for cross modality correspondence matching and deep scene recognition"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "semantic clustering for robust fine grained scene recognition"
    - "deep fishernet for object classification"
    - "convolutional color constancy"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep patch learning for weakly supervised object classification and discovery"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "fast r cnn"
    - "relaxed multiple instance svm with application to object discovery"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "exploit bounding box annotations for multi label object recognition"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"

Connected through hyperedge HE-5 (induced by source paper "body joint guided 3d deep convolutional descriptors for action recognition"):
  with members:
    - "pose for action action for pose"
    - "fine grained pose prediction normalization and recognition"
    - "unsupervised learning of video representations using lstms"
    - "p cnn pose based cnn features for action recognition"
    - "action recognition using visual attention"
    - "detecting events and key actors in multi person videos"
    - "towards good practices for very deep two stream convnets"
    - "joint training of a convolutional network and a graphical model for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "beyond low rank a data adaptive tensor completion method"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"

Connected through hyperedge HE-7 (induced by source paper "compositional model based fisher vector coding for image classification"):
  with members:
    - "approximate fisher kernels of non iid image models for image categorization"
    - "bilinear cnn models for fine grained visual recognition"
    - "mid level deep pattern mining"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "part based r cnns for fine grained category detection"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-8 (induced by source paper "semantic clustering for robust fine grained scene recognition"):
  with members:
    - "domain generalization for object recognition with multi task autoencoders"
    - "object detectors emerge in deep scene cnns"
    - "deep domain confusion maximizing for domain invariance"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)