human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mobile recognition of wikipedia featured sites using deep learning and crowd sourced imagery"):
  Abstract: Rendering Wikipedia content through mobile and augmented reality mediums can enable new forms of interaction in urban-focused user communities facilitating learning, communication and knowledge exchange. With this objective in mind, in this work we develop a mobile application that allows for the recognition of notable sites featured on Wikipedia. The application is powered by a deep neural network that has been trained on crowd-sourced imagery describing sites of interest, such as buildings, statues, museums or other physical entities that are present and visually accessible in an urban environment. We describe an end-to-end pipeline that describes data collection, model training and evaluation of our application considering online and real world scenarios. We identify a number of challenges in the site recognition task which arise due to visual similarities amongst the classified sites as well as due to noise introduce by the surrounding built environment. We demonstrate how using mobile contextual information, such as user location, orientation and attention patterns can significantly alleviate such challenges. Moreover, we present an unsupervised learning technique to de-noise crowd-sourced imagery which improves classification performance further.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "the inaturalist species classification and detection dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "using deep learning for image based plant disease detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rectified meta learning from noisy labels for robust image based plant disease diagnosis")
  Member "planet photo geolocation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "modelling uncertainty in deep learning for camera relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")

Question: Please tell me which class the source paper 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)