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 "learning category correlations for multi label image recognition with graph networks"):
  Abstract: Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies. Specifically, we introduce a plug-and-play Label Graph (LG) module to learn label correlations with word embeddings, and then utilize traditional GCN to map this graph into label-dependent object classifiers which are further applied to image features. The basic LG module incorporates two 1x1 convolutional layers and uses the dot product to generate label graphs. In addition, we propose a sparse correlation constraint to enhance the LG module and also explore different LG architectures. We validate our method on two diverse multi-label datasets: MS-COCO and Fashion550K. Experimental results show that our A-GCN significantly improves baseline methods and achieves performance superior or comparable to the state of the art.

Includes members:
  Member "linkage based face clustering via graph convolution network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep relational reasoning graph network for arbitrary shape text detection")
  Member "multi label image recognition with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inducing optimal attribute representations for conditional gans")
  Member "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "videos as space time region graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "multi label zero shot learning with structured knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bridging knowledge graphs to generate scene graphs")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pedestrian attribute recognition a survey")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "learning from noisy large scale datasets with minimal supervision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")

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)