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 "automated enriched medical concept generation for chest x ray images"):
  Abstract: Decision support tools that rely on supervised learning require large amounts of expert annotations. Using past radiological reports obtained from hospital archiving systems has many advantages as training data above manual single-class labels: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images present many challenges such as the difficulty in representing and generating long, unstructured textual information, accounting for spelling errors and repetition/redundancy, and the inconsistency across different annotators. We therefore propose to first learn visually-informative medical concepts from raw reports, and, using the concept predictions as image annotations, learn to auto-generate structured reports directly from images. We validate our approach on the OpenI [2] chest x-ray dataset, which consists of frontal and lateral views of chest x-ray images, their corresponding raw textual reports and manual medical subject heading (MeSH ) annotations made by radiologists.

Includes members:
  Member "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation")
  Member "on the automatic generation of medical imaging reports":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "justifying diagnosis decisions by deep neural networks")
  Member "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lesion harvester iteratively mining unlabeled lesions and hard negative examples at scale")
  Member "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of digital health with federated learning")
  Member "mdnet a semantically and visually interpretable medical image diagnosis network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "clara clinical report auto completion")
  Member "learning to read chest x rays recurrent neural cascade model for automated image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)