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: "fine tuning deep cnn models on specific ms coco categories"
  Abstract: Fine-tuning of a deep convolutional neural network (CNN) is often desired. This paper provides an overview of our publicly available py-faster-rcnn-ft software library that can be used to fine-tune the VGG_CNN_M_1024 model on custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset. For example, we improved the procedure so that the user does not have to look for suitable image files in the dataset by hand which can then be used in the demo program. Our implementation randomly selects images that contain at least one object of the categories on which the model is fine-tuned.

Connected through hyperedge HE-1 (induced by source paper "unsupervised hard example mining from videos for improved object detection"):
  with members:
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "s 3 fd single shot scale invariant face detector"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"):
  with members:
    - "generative adversarial networks an overview"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "exploring generalization in deep learning"
    - "building disease detection algorithms with very small numbers of positive samples"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"
    - "learning from synthetic humans"

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.AI(Artificial Intelligence)