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: "low resolution face recognition using a two branch deep convolutional neural network architecture"
  Abstract: We propose a novel couple mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET data set and compared with state-of-the-art competing methods. Our extensive experimental results show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (11.4% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with state-of-the-art super-resolution methods in terms of visual quality.

Connected through hyperedge HE-1 (induced by source paper "relational deep feature learning for heterogeneous face recognition"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "videos as space time region graphs"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "hard mining loss based convolutional neural network for face recognition"
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "arcface additive angular margin loss for deep face recognition"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (induced by source paper "cross resolution learning for face recognition"):
  with members:
    - "super identity convolutional neural network for face hallucination"
    - "deep face recognition a survey"
    - "low resolution face recognition"
    - "additive margin softmax for face verification"
    - "surveillance face recognition challenge"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving action recognition for smart hospitals using low resolution depth images"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "low resolution face recognition in the wild"
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "a review of privacy and consent management in healthcare a focus on emerging data sources"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

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)