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 "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  Abstract: Recent advancements in signal processing (SP) and machine learning, coupled with electronic medical record keeping in hospitals and the availability of extensive sets of medical images through internal/external communication systems, have resulted in a recent surge of interest in radiomics.  Radiomics , an emerging and relatively new research field, refers to extracting semiquantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models. In the near future, it is expected to be a critical component for integrating image-derived information used for personalized treatment. The conventional radiomics workflow is typically based on extracting predesigned features (also referred to as  handcrafted  or  engineered features ) from a segmented region of interest (ROI). Nevertheless, recent advancements in deep learning have inspired trends toward deep-learning-based radiomics (DLRs) (also referred to as  discovery radiomics ). In addition to the advantages of these two approaches, there are also hybrid solutions that exploit the potential of multiple data sources. Considering the variety of approaches to radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This article provides a unique interdisciplinary perspective on radiomics by discussing state-of-the-art SP solutions in the context of radiomics.

Includes members:
  Member "semantic segmentation of pathological lung tissue with dilated fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an abstraction model for semantic segmentation algorithms")
  Member "brain tumor type classification via capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "radiomics strategies for risk assessment of tumour failure in head and neck cancer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for automated medical image analysis")
  Member "prostate cancer diagnosis using deep learning with 3d multiparametric mri":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prostate cancer detection using deep convolutional neural networks")
  Member "understanding convolution for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence")

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)