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 "ace net biomedical image segmentation with augmented contracting and expansive paths"):
  Abstract: Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e.g., symmetric contracting and expansive paths as well as lateral skip-connections. It remains a research direction to devise novel architectures to further benefit the segmentation. In this paper, we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths. In particular, we augment the paths by the recently proposed advanced techniques including ASPP, dense connection and deep supervision mechanisms, and novel connections such as directly connecting the raw image to the expansive side. With these augmentations, ACE-net can utilize features from multiple sources, scales and reception fields to segment while still maintains a relative simple architecture. Experiments on two typical biomedical segmentation tasks validate its effectiveness, where highly competitive results are obtained in both tasks while ACE-net still runs fast at inference.

Includes members:
  Member "deep supervision with additional labels for retinal vessel segmentation task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review")
  Member "learning a discriminative feature network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "learning steerable filters for rotation equivariant cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field")
  Member "multi stage multi recursive input fully convolutional networks for neuronal boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boundary aware feature propagation for scene segmentation")
  Member "rethinking atrous convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")
  Member "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semantic transferable weakly supervised endoscopic lesions segmentation")

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)