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 "fusionnet incorporating shape and texture for abnormality detection in 3d abdominal ct scans"):
  Abstract: Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer. Taking the fact that the existence of tumor can affect both the shape and the texture of pancreas, we design a system to extract the shape and texture feature at the same time for detecting PDAC. In this paper we propose a two-stage method for this 3D classification task. First, we segment the pancreas into a binary mask. Second, a FusionNet is proposed to take both the binary mask and CT image as input and perform a binary classification. The optimal architecture of the FusionNet is obtained by searching a pre-defined functional space. We show that the classification results using either shape or texture information are complementary, and by fusing them with the optimized architecture, the performance improves by a large margin. Our method achieves a specificity of 97% and a sensitivity of 92% on 200 normal scans and 136 scans with PDAC.

Includes members:
  Member "3d semi supervised learning with uncertainty aware multi view co training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "joint shape representation and classification for detecting pdac":
    also connected through other hyperedges:
      (none)
  Member "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans")
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "a 3d coarse to fine framework for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object contextual representations for semantic segmentation")
  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 "personalized pancreatic tumor growth prediction via group learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spatial temporal convolutional lstms for tumor growth prediction by learning 4d longitudinal patient 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)