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: "3fabrec fast few shot face alignment by reconstruction"
  Abstract: Current supervised frameworks for facial landmark detection require a large amount of training data and due to the massive number of parameters may suffer from overfitting to the specific datasets. We introduce a semi-supervised method in which the crucial idea is to first generate implicit knowledge about the face appearance & shape from the large amounts of unlabeled images of faces available today. In a first, unsupervised stage, we train an adversarial autoencoder to reconstruct faces via a low-dimensional, latent face-representation vector. In a second, supervised stage, we augment the generator-decoder pipeline with interleaved transfer layers in order to both reconstruct the face and a probabilistic landmark heatmap. We show that this framework (3FabRec) achieves state-of-the-art performance on popular benchmarks, such as 300-W, AFLW, and WLFW. Importantly, due to the power of the implicit face representation, our framework achieves impressive landmark localization accuracy from only a few percent of training data to as low as even 10 images. As the interleaved layers only add a small number of parameters to the encoder, inference runs at several hundred FPS on a GPU.

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)