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: "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"
  Abstract: This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. We propose the two-stage approach, in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. We modified the MobileNet, which is preliminarily trained to perform face recognition, in order to additionally recognize age and gender. In the second stage of our approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The born year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. We experimentally demonstrated that our facial clustering quality is competitive with the state-of-the-art neural networks, though our implementation is much computationally cheaper. Moreover, our approach is characterized by more accurate video-based age/gender recognition when compared to the publicly available models.

Connected through hyperedge HE-1 (induced by source paper "user preference prediction in visual data on mobile devices"):
  with members:
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"
    - "visual discovery at pinterest"
    - "neural aggregation network for video face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-2 (induced by source paper "event recognition with automatic album detection based on sequential processing neural attention and image captioning"):
  with members:
    - "user preference prediction in visual data on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "recognizing and curating photo albums via event specific image importance"

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)