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: "an interpretable model for scene graph generation"
  Abstract: We propose an efficient and interpretable scene graph generator. We consider three types of features: visual, spatial and semantic, and we use a late fusion strategy such that each feature's contribution can be explicitly investigated. We study the key factors about these features that have the most impact on the performance, and also visualize the learned visual features for relationships and investigate the efficacy of our model. We won the champion of the OpenImages Visual Relationship Detection Challenge on Kaggle, where we outperform the 2nd place by 5\% (20\% relatively). We believe an accurate scene graph generator is a fundamental stepping stone for higher-level vision-language tasks such as image captioning and visual QA, since it provides a semantic, structured comprehension of an image that is beyond pixels and objects.

Connected through hyperedge HE-1 (induced by source paper "differentiable scene graphs"):
  with members:
    - "spatio temporal action graph networks"
    - "dynamic graph attention for referring expression comprehension"
    - "generating easy to understand referring expressions for target identifications"
    - "vrr vg refocusing visually relevant relationships"
    - "learning canonical representations for scene graph to image generation"
    - "adaptive reconstruction network for weakly supervised referring expression grounding"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

Connected through hyperedge HE-2 (induced by source paper "graphical contrastive losses for scene graph parsing"):
  with members:
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "mattnet modular attention network for referring expression comprehension"
    - "large scale visual relationship understanding"
    - "query guided regression network with context policy for phrase grounding"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (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"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "learn to pay attention"

Connected through hyperedge HE-4 (induced by source paper "2nd place solution to the gqa challenge 2019"):
  with members:
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "compositional attention networks for machine reasoning"
    - "tvqa localized compositional video question answering"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-5 (induced by source paper "large scale visual relationship understanding"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"
    - "vse improving visual semantic embeddings with hard negatives"

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)