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: "deep neural networks for pattern recognition"
  Abstract: In the field of pattern recognition research, the method of using deep neural networks based on improved computing hardware recently attracted attention because of their superior accuracy compared to conventional methods. Deep neural networks simulate the human visual system and achieve human equivalent accuracy in image classification, object detection, and segmentation. This chapter introduces the basic structure of deep neural networks that simulate human neural networks. Then we identify the operational processes and applications of conditional generative adversarial networks, which are being actively researched based on the bottom-up and top-down mechanisms, the most important functions of the human visual perception process. Finally, recent developments in training strategies for effective learning of complex deep neural networks are addressed.

Connected through hyperedge HE-1 (induced by source paper "small target detection for search and rescue operations using distributed deep learning and synthetic data generation"):
  with members:
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks"
    - "automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model"
    - "feature fused ssd fast detection for small objects"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "detecting small signs from large images"
    - "unrealcv connecting computer vision to unreal engine"
    - "mocap guided data augmentation for 3d pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "detection and identification of objects and humans in thermal images"):
  with members:
    - "unsupervised segmentation of fire and smoke from infra red videos"
    - "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"
    - "very deep convolutional networks for large scale image 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)