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 "visual relationship detection with language prior and softmax"):
  Abstract: Visual relationship detection is an intermediate image understanding task that detects two objects and classifies a predicate that explains the relationship between two objects in an image. The three components are linguistically and visually correlated (e.g. "wear" is related to "person" and "shirt", while "laptop" is related to "table" and "on") thus, the solution space is huge because there are many possible cases between them. Language and visual modules are exploited and a sophisticated spatial vector is proposed. The models in this work outperformed the state of arts without costly linguistic knowledge distillation from a large text corpus and building complex loss functions. All experiments were only evaluated on Visual Relationship Detection and Visual Genome dataset.

Includes members:
  Member "vip cnn visual phrase guided convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deep reinforcement learning based image captioning with embedding reward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "detecting visual relationships with deep relational networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deep variation structured reinforcement learning for visual relationship and attribute detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "scene graph generation by iterative message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "visual relationship detection with internal and external linguistic knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "scene graph generation from objects phrases and region captions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)