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: "discriminative convolutional fisher vector network for action recognition"
  Abstract: In this work we propose a novel neural network architecture for the problem of human action recognition in videos. The proposed architecture expresses the processing steps of classical Fisher vector approaches, that is dimensionality reduction by principal component analysis (PCA) projection, Gaussian mixture model (GMM) and Fisher vector descriptor extraction, as network layers. By contrast to other methods where these steps are performed consecutively and the corresponding parameters are learned in an unsupervised manner, having them defined as a single neural network allows us to refine the whole model discriminatively in an end to end fashion. Furthermore, we show that the proposed architecture can be used as a replacement for the fully connected layers in popular convolutional networks achieving a comparable classification performance, or even significantly surpassing the performance of similar architectures while reducing the total number of trainable parameters by a factor of 5. We show that our method achieves significant improvements in comparison to the classical chain.

Connected through hyperedge HE-1 (induced by source paper "tarn temporal attentive relation network for few shot and zero shot action recognition"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "prototypical networks for few shot learning"
    - "the kinetics human action video dataset"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "unsupervised human action detection by action matching"
    - "a decomposable attention model for natural language inference"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "residual network based aggregation model for skin lesion classification"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "deep fishernet for object classification"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "schinet automatic estimation of symptoms of schizophrenia from facial behaviour analysis"):
  with members:
    - "from facial expression recognition to interpersonal relation prediction"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "semi supervised fisher vector network"):
  with members:
    - "youtube 8m a large scale video classification benchmark"
    - "very deep convolutional networks for large scale image recognition"

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)