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 "tan temporal affine network for real time left ventricle anatomical structure analysis based on 2d ultrasound videos"):
  Abstract: With superiorities on low cost, portability, and free of radiation, echocardiogram is a widely used imaging modality for left ventricle (LV) function quantification. However, automatic LV segmentation and motion tracking is still a challenging task. In addition to fuzzy border definition, low contrast, and abounding artifacts on typical ultrasound images, the shape and size of the LV change significantly in a cardiac cycle. In this work, we propose a temporal affine network (TAN) to perform image analysis in a warped image space, where the shape and size variations due to the cardiac motion as well as other artifacts are largely compensated. Furthermore, we perform three frequent echocardiogram interpretation tasks simultaneously: standard cardiac plane recognition, LV landmark detection, and LV segmentation. Instead of using three networks with one dedicating to each task, we use a multi-task network to perform three tasks simultaneously. Since three tasks share the same encoder, the compact network improves the segmentation accuracy with more supervision. The network is further finetuned with optical flow adjusted annotations to enhance motion coherence in the segmentation result. Experiments on 1,714 2D echocardiographic sequences demonstrate that the proposed method achieves state-of-the-art segmentation accuracy with real-time efficiency.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "medgan medical image translation using gans")
  Member "a comprehensive approach for learning based fully automated inter slice motion correction for short axis cine cardiac mr image stacks":
    also connected through other hyperedges:
      (none)
  Member "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a spatiotemporal volumetric interpolation network for 4d dynamic medical image")
  Member "left ventricle segmentation and quantification from cardiac cine mr images via multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a distance map regularized cnn for cardiac cine mr image segmentation")
  Member "iterative segmentation from limited training data applications to congenital heart disease":
    also connected through other hyperedges:
      (none)
  Member "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "kpnet towards minimal face detector")
  Member "automated segmentation on the entire cardiac cycle using a deep learning work flow":
    also connected through other hyperedges:
      (none)

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)