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 "user preference prediction in visual data on mobile devices"):
  Abstract: In this paper we consider the user modeling given the photos and videos from the gallery on a mobile device. We propose the novel user preference prediction engine based on scene understanding, object detection and face recognition. At first, all faces in a gallery are clustered and all private photos and videos with faces from large clusters are processed on the embedded system in offline mode. Other photos are sent to the remote server to be analyzed by very deep models. The visual features of each photo are aggregated into a single user descriptor using the neural attention block. The proposed pipeline is implemented for the Android mobile platform. Experimental results with a subset of Amazon Home and Kitchen, Places2 and Open Images datasets demonstrate the possibility to process images very efficiently without accuracy degradation.

Includes members:
  Member "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "event recognition with automatic album detection based on sequential processing neural attention and image captioning")
  Member "unconstrained face identification using maximum likelihood of distances between deep off the shelf features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet")
  Member "deep learning based large scale visual recommendation and search for e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "featurenms non maximum suppression by learning feature embeddings")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learnable pooling with context gating for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pic permutation invariant convolution for recognizing long range activities")
  Member "visual discovery at pinterest":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "handling position bias for unbiased learning to rank in hotels search")
  Member "neural aggregation network for video face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards universal representation learning for deep face recognition")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")

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)