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 "webcaricature a benchmark for caricature face recognition"):
  Abstract: Studying caricature recognition is fundamentally important to understanding of face perception. However, little research has been conducted in the computer vision community, largely due to the shortage of suitable datasets. In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Evaluation protocols are also offered together with their baseline performances on the dataset to allow fair comparisons. Besides, a framework for caricature face recognition is presented to make a thorough analyze of the challenges of caricature recognition. By analyzing the challenges, the goal is to show problems that worth to be further investigated. Additionally, based on the evaluation protocols and the framework, baseline performances of various state-of-the-art algorithms are provided. A conclusion is that there is still a large space for performance improvement and the analyzed problems still need further investigation.

Includes members:
  Member "shared representation learning for heterogeneous face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "consistency of the group lasso and multiple kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint user selection and precoding in multiuser mimo systems via group lasso")

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)