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 "computational anatomy for multi organ analysis in medical imaging a review"):
  Abstract: The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.

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 generic approach to lung field segmentation from chest radiographs using deep space and shape learning":
    also connected through other hyperedges:
      (none)
  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 learning and its application to medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "addressing the memory bottleneck in ai model training")
  Member "tracking the trackers an analysis of the state of the art in multiple object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unovost unsupervised offline video object segmentation and tracking")
  Member "hierarchical 3d fully convolutional networks for multi organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "towards automatic abdominal multi organ segmentation in dual energy ct using cascaded 3d fully convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review")

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)