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 "kneel knee anatomical landmark localization using hourglass networks"):
  Abstract: This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA). Landmark localization can be viewed as regression problem, where the landmark position is directly predicted by using the region of interest or even full-size images leading to large memory footprint, especially in case of high resolution medical images. In this work, we propose an efficient deep neural networks framework with an hourglass architecture utilizing a soft-argmax layer to directly predict normalized coordinates of the landmark points. We provide an extensive evaluation of different regularization techniques and various loss functions to understand their influence on the localization performance. Furthermore, we introduce the concept of transfer learning from low-budget annotations, and experimentally demonstrate that such approach is improving the accuracy of landmark localization. Compared to the prior methods, we validate our model on two datasets that are independent from the train data and assess the performance of the method for different stages of OA severity. The proposed approach demonstrates better generalization performance compared to the current state-of-the-art.

Includes members:
  Member "geometric image correspondence verification by dense pixel matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "glu net global local universal network for dense flow and correspondences")
  Member "facial landmark detection a literature survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "facial landmark correlation analysis")
  Member "wing loss for robust facial landmark localisation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kpnet towards minimal face detector")
  Member "dgc net dense geometric correspondence network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")
  Member "a general and adaptive robust loss function":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "derivative manipulation for general example weighting")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")

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)