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 "sosnet second order similarity regularization for local descriptor learning"):
  Abstract: Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors. In this work, we explore the potential of SOS in the field of descriptor learning by building upon the intuition that a positive pair of matching points should exhibit similar distances with respect to other points in the embedding space. Thus, we propose a novel regularization term, named Second Order Similarity Regularization (SOSR), that follows this principle. By incorporating SOSR into training, our learned descriptor achieves state-of-the-art performance on several challenging benchmarks containing distinct tasks ranging from local patch retrieval to structure from motion. Furthermore, by designing a von Mises-Fischer distribution based evaluation method, we link the utilization of the descriptor space to the matching performance, thus demonstrating the effectiveness of our proposed SOSR. Extensive experimental results, empirical evidence, and in-depth analysis are provided, indicating that SOSR can significantly boost the matching performance of the learned descriptor.

Includes members:
  Member "directional statistics based deep metric learning for image classification and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")
  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 "geodesc learning local descriptors by integrating geometry constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "learning spread out local feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "hpatches a benchmark and evaluation of handcrafted and learned local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "working hard to know your neighbor s margins local descriptor learning loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "cnn image retrieval learns from bow unsupervised fine tuning with hard examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)