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 learning vs traditional computer vision"
  Abstract: Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised

Connected through hyperedge HE-1 (induced by source paper "signed input regularization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial training generalizes data dependent spectral norm regularization"
    - "improved inference via deep input transfer"
    - "metamixup learning adaptive interpolation policy of mixup with meta learning"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "data interpolating prediction alternative interpretation of mixup"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-2 (induced by source paper "learning from learning machines optimisation rules and social norms"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "incomplete contracting and ai alignment"
    - "recent trends in deep learning based natural language processing"
    - "a survey on deep learning in medical image analysis"
    - "deep learning in bioinformatics"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep image scaling up image recognition"
    - "deep multimodal learning for audio visual speech 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)