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: "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
  Abstract: In this paper we propose the two-stage approach of organizing information in video surveillance systems. At first, the faces are detected in each frame and a video stream is split into sequences of frames with face region of one person. Secondly, these sequences (tracks) that contain identical faces are grouped using face verification algorithms and hierarchical agglomerative clustering. Gender and age are estimated for each cluster (person) in order to facilitate the usage of the organized video collection. The particular attention is focused on the aggregation of features extracted from each frame with the deep convolutional neural networks. The experimental results of the proposed approach using YTF and IJB-A datasets demonstrated that the most accurate and fast solution is achieved for matching of normalized average of feature vectors of all frames in a track.

Connected through hyperedge HE-1 (induced by source paper "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"):
  with members:
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "face clustering representation and pairwise constraints"
    - "merge or not learning to group faces via imitation learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learnable pooling with context gating for video classification"

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:
    - "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "viplfacenet an open source deep face recognition sdk"
    - "do we really need to collect millions of faces for effective face 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)