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: "face recognition a novel multi level taxonomy based survey"
  Abstract: In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which may define future research directions for face recognition.

Connected through hyperedge HE-1 (induced by source paper "attention aware fusion rgb d face recognition"):
  with members:
    - "classification of hand movements from eeg using a deep attention based lstm network"
    - "capsule attention for multimodal eeg and eog spatiotemporal representation learning with application to driver vigilance estimation"
    - "robust rgb d face recognition using attribute aware loss"
    - "deep face recognition a survey"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "poseidon face from depth for driver pose estimation"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-2 (induced by source paper "novel long short term memory cell architectures application to light field face recognition"):
  with members:
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a double deep spatio angular learning framework for light field based face recognition"):
  with members:
    - "deep face recognition a survey"
    - "personalized age progression with bi level aging dictionary learning"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "regularizing and optimizing lstm language models"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "improving shadow suppression for illumination robust face recognition"
    - "a comprehensive analysis of deep learning based representation for face 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)