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 "icartoonface a benchmark of cartoon person recognition"):
  Abstract: Cartoons receive increasingly attention and have a huge global market. Cartoon person recognition has a wealth of application scenarios. However, there is no large and high quality dataset for cartoon person recognition. It limit the development of recognition algorithms. In this paper, we propose the first large unconstrained cartoon database called iCartoonFace. We have released the dataset publicly available to promote cartoon person recognition research\footnote{The dataset can be applied by sending email to zhengyi01@qiyi.com}. The dataset contains 68,312 images of 2,639 identities. The dataset consists of persons which come from cartoon videos. The samples are extracted from public available images on website and online videos on iQiYi company. All images pass through a careful manual annotation process. We evaluated the state-of-the-art image classification and face recognition algorithms on the iCartoonFace dataset as a baseline. A dataset fusion method which utilize face feature to improve the performance of cartoon recognition task is proposed. Experimental performance show that the performance of baseline models much worse than human performance. The proposed dataset fusion method achieves a 4.74% improvement over the baseline model. In a word, state-of-the-art algorithms for classification and recognition are far from being perfect for unconstrained cartoon person recognition.

Includes members:
  Member "cosface large margin cosine loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "sphereface deep hypersphere embedding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "billion scale similarity search with gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "webcaricature a benchmark for caricature face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mw gan multi warping gan for caricature generation with multi style geometric exaggeration")
  Member "fully adaptive feature sharing in multi task networks with applications in person attribute classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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)