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: "scene graph prediction with limited labels"
  Abstract: Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by 5.16 recall@100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R^2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.

Connected through hyperedge HE-1 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "explainable video action reasoning via prior knowledge and state transitions"

Connected through hyperedge HE-2 (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"
    - "triplet aware scene graph embeddings"

Connected through hyperedge HE-3 (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"
    - "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"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "learning predicates as functions to enable few shot scene graph prediction"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "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-5 (induced by source paper "learning effective visual relationship detector on 1 gpu"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "deep high resolution representation learning for visual recognition"
    - "deep high resolution representation learning for human pose estimation"
    - "graph r cnn for scene graph generation"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "neural motifs scene graph parsing with global context"

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)