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: "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
  Abstract: Abstract   The paper deals with unconstrained face recognition task for the small sample size problem based on computation of distances between high-dimensional off-the-shelf features extracted by deep convolution neural network. We present the novel statistical recognition method, which maximizes the likelihood (joint probabilistic density) of the distances to all reference images from the gallery set. This likelihood is estimated with the known asymptotically normal distribution of the Kullback–Leibler discrimination between nonnegative features. Our approach penalizes the individuals if their feature vectors do not behave like the features of observed image in the space of dissimilarities of the gallery images. We provide the experimental study with the LFW (Labeled Faces in the Wild), YTF (YouTube Faces) and IJB-A (IARPA Janus Benchmark A) datasets and the state-of-the-art deep learning-based feature extractors (VGG-Face, VGGFace2, ResFace-101, CenterFace and Light CNN). It is demonstrated, that the proposed approach can be applied with traditional distances in order to increase accuracy in 0.3–5.5% when compared to known methods, especially if the training and testing images are significantly different.

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:
    - "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"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "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 "user preference prediction in visual data on mobile devices"):
  with members:
    - "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"
    - "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"

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)