human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "natural language guided visual relationship detection"
  Abstract: Reasoning about the relationships between object pairs in images is a crucial task for holistic scene understanding. Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features. However, each kind of relationships has a wide variety of object combination and each pair of objects has diverse interactions. Obtaining sufficient training samples for all possible relationship categories is difficult and expensive. In this work, we propose a natural language guided framework to tackle this problem. We propose to use a generic bi-directional recurrent neural network to predict the semantic connection between the participating objects in the relationship from the aspect of natural language. The proposed simple method achieves the state-of-the-art on the Visual Relationship Detection (VRD) and Visual Genome datasets, especially when predicting unseen relationships (e.g. recall improved from 76.42% to 89.79% on VRD zero-shot testing set).

Connected through hyperedge HE-1 (induced by source paper "visual relationships as functions enabling few shot scene graph prediction"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "scene graph prediction with limited labels"
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "image generation from scene graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-2 (induced by source paper "scene graph generation with external knowledge and image reconstruction"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "graph r cnn for scene graph generation"
    - "scene graph parsing as dependency parsing"
    - "auto encoding scene graphs for image captioning"
    - "unpaired image captioning by language pivoting"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "exploring the semantics for visual relationship detection"):
  with members:
    - "detecting unseen visual relations using analogies"
    - "graph r cnn for scene graph generation"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "deeply supervised multimodal attentional translation embeddings for visual relationship detection"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "context dependent diffusion network for visual relationship detection"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "detecting visual relationships using box attention"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"

Connected through hyperedge HE-5 (induced by source paper "factorizable net an efficient subgraph based framework for scene graph generation"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "towards context aware interaction recognition"
    - "visual question generation as dual task of visual question answering"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "visual graphs from motion vgfm scene understanding with object geometry reasoning"):
  with members:
    - "scenarios a new representation for complex scene understanding"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "dynamic routing between capsules"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "visual translation embedding network for visual relation detection"

Question: Please tell me which class the center node 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)