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 "automatic dataset annotation to learn cnn pore description for fingerprint recognition"):
  Abstract: High-resolution fingerprint recognition often relies on sophisticated matching algorithms based on hand-crafted keypoint descriptors, with pores being the most common keypoint choice. Our method is the opposite of the prevalent approach: we use instead a simple matching algorithm based on robust local pore descriptors that are learned from the data using a CNN. In order to train this CNN in a fully supervised manner, we describe how the automatic alignment of fingerprint images can be used to obtain the required training annotations, which are otherwise missing in all publicly available datasets. This improves the state-of-the-art recognition results for both partial and full fingerprints in a public benchmark. To confirm that the observed improvement is due to the adoption of learned descriptors, we conduct an ablation study using the most successful pore descriptors previously used in the literature. All our code is available at this https URL

Includes members:
  Member "improving fingerprint pore detection with a small fcn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for cross sensor pore detection in high resolution fingerprint images")
  Member "local descriptors optimized for average precision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "realistic evaluation of deep semi supervised learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking batch normalization in transformers")
  Member "working hard to know your neighbor s margins local descriptor learning loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "understanding deep learning requires rethinking generalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "learning deep embeddings with histogram loss":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting training strategies and generalization performance in deep metric 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)