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: "a double deep spatio angular learning framework for light field based face recognition"
  Abstract: Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional neural network (CNN). The VGG-16 network uses different face viewpoints rendered from a full light field image, which are organised as a pseudo-video sequence. A comprehensive set of experiments has been conducted with the IST-EURECOM light field face database, for varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "face recognition a novel multi level taxonomy based survey"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "ring loss convex feature normalization for face recognition"
    - "face recognition via centralized coordinate learning"
    - "nonlinear 3d face morphable model"
    - "a novel channel pruning method for deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "light field saliency detection with deep convolutional networks"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-3 (induced by source paper "novel long short term memory cell architectures application to light field face recognition"):
  with members:
    - "face recognition a novel multi level taxonomy based survey"
    - "lstm a search space odyssey"
    - "regularizing and optimizing lstm language models"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

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)