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 "adaptive graph convolutional network with attention graph clustering for co saliency detection"):
  Abstract: Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical merits. First, we propose a graph convolutional network design to extract information cues to characterize the intra- and interimage correspondence. Second, we develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. Third, we present a unified framework with encoder-decoder structure to jointly train and optimize the graph convolutional network, attention graph cluster, and co-saliency detection decoder in an end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method obtains significant improvements over the state-of-the-arts on most of them.

Includes members:
  Member "scene graph generation with external knowledge and image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object centric image generation from layouts")
  Member "learning graph embedding with adversarial training methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors")
  Member "attributed graph clustering a deep attentional embedding approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural deep clustering network")
  Member "label efficient semi supervised learning via graph filtering":
    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 comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "graph neural networks a review of methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "review of visual saliency detection with comprehensive information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "global context aware progressive aggregation network for salient object detection")
  Member "iterative visual reasoning beyond convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bridging knowledge graphs to generate scene graphs")

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)