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 relationships as functions enabling few shot scene graph prediction"):
  Abstract: Scene graph prediction --- classifying the set of objects and predicates in a visual scene --- requires substantial training data. The long-tailed distribution of relationships can be an obstacle for such approaches, however, as they can only be trained on the small set of predicates that carry sufficient labels. We introduce the first scene graph prediction model that supports few-shot learning of predicates, enabling scene graph approaches to generalize to a set of new predicates. First, we introduce a new model of predicates as functions that operate on object features or image locations. Next, we define a scene graph model where these functions are trained as message passing protocols within a new graph convolution framework. We train the framework with a frequently occurring set of predicates and show that our approach outperforms those that use the same amount of supervision by 1.78 at recall@50 and performs on par with other scene graph models. Next, we extract object representations generated by the trained predicate functions to train few-shot predicate classifiers on rare predicates with as few as 1 labeled example. When compared to strong baselines like transfer learning from existing state-of-the-art representations, we show improved 5-shot performance by 4.16 recall@1. Finally, we show that our predicate functions generate interpretable visualizations, enabling the first interpretable scene graph model.

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 "scene graph prediction with limited labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action genome actions as composition of spatio temporal scene graphs")
  Member "referring relationships":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "graph r cnn for scene graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "image generation from scene graphs":
    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 "vip cnn visual phrase guided convolutional neural network":
    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 "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised question decomposition for question answering")
  Member "neural motifs scene graph parsing with global context":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)