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 "dynamic multimodal instance segmentation guided by natural language queries"):
  Abstract: We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either (\textit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by (\textit{ii}) mapping the expression to a space in which it can be thought of as a filter, whose response is directly related to the presence of the object at a given spatial coordinate in the image, so that a convolution can be applied to look for the object. We propose a novel method that integrates these two insights in order to fully exploit the recursive nature of language. Additionally, during the upsampling process, we take advantage of the intermediate information generated when downsampling the image, so that detailed segmentations can be obtained. We compare our method against the state-of-the-art approaches in four standard datasets, in which it surpasses all previous methods in six of eight of the splits for this task.

Includes members:
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity")
  Member "recurrent multimodal interaction for referring image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual convolutional lstm network for referring image segmentation")
  Member "semantic compositional networks for visual captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "review networks for caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vision and language from visual perception to content creation")
  Member "segmentation from natural language expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "graph structured representations for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "utilizing large scale vision and text datasets for image segmentation from referring expressions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recurrent multimodal interaction for referring image 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)