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 "mining domain knowledge improved framework towards automatically standardizing anatomical structure nomenclature in radiotherapy"):
  Abstract: Automatically standardizing nomenclature for anatomical structures in radiotherapy (RT) clinical data is an unmet urgent need in the era of big data and artificial intelligence. Existing methods either can hardly handle cross-institutional datasets or suffer from heavy imbalance and poor-quality delineation in clinical RT datasets. To solve these problems, we propose an automated structure nomenclature standardization framework, 3DNNV, which consists of an improved data processing strategy (ASAC/Voting) and an optimized feature extraction module to simulate clinicians' domain knowledge and recognition mechanisms to identify heavily imbalanced small-volume organs at risk (OARs) better than other methods. We used partial data from an open-source head-and-neck cancer dataset (HN_PETCT) to train the model, then tested the model on three cross-institutional datasets to demonstrate its generalizability. 3DNNV outperformed the baseline model (ResNet50), achieving a significantly higher average true positive rate (TPR) on the three test datasets (+8.27%, +2.39%, +5.53%). More importantly, the 3DNNV outperformed the baseline, 28.63% to 91.17%, on the F1 score of a small-volume OAR with only 9 training samples, when tested on the HN_UTSW dataset. The developed framework can be used to help standardizing structure nomenclature to facilitate data-driven clinical research in cancer radiotherapy.

Includes members:
  Member "res2net a new multi scale backbone architecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography")
  Member "benchmark analysis of representative deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is the state of neural network pruning")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "bagan data augmentation with balancing gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards fair cross domain adaptation via generative learning")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)