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 "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  Abstract: This paper presents a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. Multi-scale contextual information is effective for pixel-level label prediction, i.e. image segmentation. However, such important information is only partially exploited by the existing methods. In this paper, we propose a new network architecture for multi-scale feature abstraction, which integrates pyramid feature analysis into an image segmentation model. To bridge the semantic gap caused by directly merging features from different scales, an equal convolutional depth mechanism is proposed. In addition, we develop a deep supervision mechanism for refining outputs in different scales. To fully leverage the segmentation features from different scales, we design an adaptive weighting layer to fuse the outputs in an automatic fashion. All these features together integrate into a pyramid-input pyramid-output network for efficient feature extraction. Last but not least, to alleviate the hunger for fully annotated data in training deep segmentation models, a unified training strategy is proposed to train one segmentation model on multiple partially labeled datasets for multi-organ segmentation with a novel target adaptive loss. Our proposed method was evaluated on four publicly available datasets, including BTCV, LiTS, KiTS and Spleen, where very promising performance has been achieved. The source code of this work is publicly shared at this https URL for others to easily reproduce the work and build their own models with the introduced mechanisms.

Includes members:
  Member "boundary weighted domain adaptive neural network for prostate mr image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic guided encoder feature learning for blurry boundary delineation")
  Member "learning a deep convnet for multi label classification with partial labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  Member "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "feature fusion encoder decoder network for automatic liver lesion segmentation":
    also connected through other hyperedges:
      (none)
  Member "ce net context encoder network for 2d medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "the liver tumor segmentation benchmark lits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives")
  Member "abdominal multi organ segmentation with organ attention networks and statistical fusion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep neural network perception models and robust autonomous driving systems")

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)