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 "hierarchical attention networks for medical image segmentation"):
  Abstract: The medical image is characterized by the inter-class indistinction, high variability, and noise, where the recognition of pixels is challenging. Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation. Concretely, an HA module embedded in the HANet captures context information from neighbors of multiple levels, where these neighbors are extracted from the high-order graph. In the high-order graph, there will be an edge between two nodes only if the correlation between them is high enough, which naturally reduces the noisy attention information caused by the inter-class indistinction. The proposed HA module is robust to the variance of input and can be flexibly inserted into the existing convolution neural networks. We conduct experiments on three medical image segmentation tasks including optic disc/cup segmentation, blood vessel segmentation, and lung segmentation. Extensive results show our method is more effective and robust than the existing state-of-the-art methods.

Includes members:
  Member "dunet a deformable network for retinal vessel segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the evaluation and real world usage scenarios of deep vessel segmentation for retinography")
  Member "expectation maximization attention networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic flow for fast and accurate scene parsing")
  Member "patch based output space adversarial learning for joint optic disc and cup segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "a simple pooling based design for real time salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "ce net context encoder network for 2d medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "et net a generic edge attention guidance network for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "fast interactive object annotation with curve gcn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep snake for real time instance 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)