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: "large age gap face verification by feature injection in deep networks"
  Abstract: This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Finetuning is performed in a Siamese architecture using a contrastive loss function. A feature injection layer is introduced to boost verification accuracy, showing the ability of the DCNN to learn a similarity metric leveraging external features. Experimental results on the LAG dataset show that our method is able to outperform the face verification solutions in the state of the art considered.

Connected through hyperedge HE-1 (induced by source paper "assisted excitation of activations a learning technique to improve object detectors"):
  with members:
    - "yolov3 an incremental improvement"
    - "triply supervised decoder networks for joint detection and segmentation"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "single shot text detector with regional attention"
    - "single shot object detection with enriched semantics"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "multitask painting categorization by deep multibranch neural network"):
  with members:
    - "visual descriptors for content based retrieval of remote sensing images"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "large scale classification of fine art paintings learning the right metric on the right feature"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "recognizing image style"

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)