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: "pilot comparative study of different deep features for palmprint identification in low quality images"
  Abstract: Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of visual recognition. In this paper, we present a comparative study of three popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the problem of palmprint identification in low-quality imagery and apply Support Vector Machines (SVMs) with all of the compared models. For the comparison, we use the MOHI palmprint image database whose images are characterized by low contrast, shadows, and varying illumination, scale, translation and rotation. Another, high-quality database called COEP is also considered to study the recognition gap between high-quality and low-quality imagery. Our experiments show that the deeper pre-trained CNN models, e.g., VGG-16 and VGG-19, tend to extract highly distinguishable features that recognize low-quality palmprints more efficiently than the less deep networks such as AlexNet. Furthermore, our experiments on the two databases using various models demonstrate that the features extracted from lower-level fully connected layers provide higher recognition rates than higher-layer features. Our results indicate that different pre-trained models can be efficiently used in touchless identification systems with low-quality palmprint images.

Connected through hyperedge HE-1 (induced by source paper "towards unconstrained palmprint recognition on consumer devices a literature review"):
  with members:
    - "palmprint recognition in uncontrolled and uncooperative environment"
    - "a study on wrist identification for forensic investigation"
    - "11k hands gender recognition and biometric identification using a large dataset of hand images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "convolutional pose machines"
    - "ternary weight networks"

Connected through hyperedge HE-2 (induced by source paper "detailed investigation of deep features with sparse representation and dimensionality reduction in cbir a comparative study"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "deep image mining for diabetic retinopathy screening"
    - "selective deep convolutional features for image retrieval"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"
    - "visual speech recognition"

Connected through hyperedge HE-3 (induced by source paper "deep face image retrieval a comparative study with dictionary learning"):
  with members:
    - "detailed investigation of deep features with sparse representation and dimensionality reduction in cbir a comparative study"
    - "an analysis of deep neural network models for practical applications"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using deep convolutional networks"
    - "optimization with sparsity inducing penalties"

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)