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 "semi supervised multi organ segmentation via deep multi planar co training"):
  Abstract: Multi-organ segmentation is a critical problem in medical image analysis due to its great value for computer-aided diagnosis, computer-aided surgery, and radiation therapy. Although fully-supervised segmentation methods can achieve good performance, they usually require a large amount of 3D data, such as CT scans, with voxel-wised annotations which are usually difficult, expensive, and slow to obtain. By contrast, large unannotated datasets of CT images are available. Inspired by the well-known semi-supervised learning framework co-training, we propose multi-planar co-training (MPCT), to generate more reliable pseudo-labels by enforcing consistency among multiple planes, i.e., saggital, coronal, and axial planes, of 3D unlabeled medical data, which play a vital role in our framework. Empirical results show that generating pseudo-labels by the multi-planar fusion rather than a single plane leads to a significant performance gain. We evaluate our approach on a new collected dataset and show that MPCT boosts the performance of a typical segmentation model, fully convolutional networks, by a large margin, when only a small set of labeled 3D data is available, i.e., 77.49% vs. 73.14%.

Includes members:
  Member "abdominal multi organ segmentation with organ attention networks and statistical fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "deep co training for semi supervised image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "pcl proposal cluster learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "deep supervision for pancreatic cyst segmentation in abdominal ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "phase collaborative network for two phase medical image segmentation")
  Member "data distillation towards omni supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma")

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)