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: "the more you know using knowledge graphs for image classification"
  Abstract: One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "bridging knowledge graphs to generate scene graphs"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "entity relation and event extraction with contextualized span representations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "an empirical study on leveraging scene graphs for visual question answering"
    - "class balanced loss based on effective number of samples"
    - "question answering as global reasoning over semantic abstractions"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "agl a scalable system for industrial purpose graph machine learning"):
  with members:
    - "dsslp a distributed framework for semi supervised link prediction"
    - "heterogeneous graph neural networks for malicious account detection"
    - "fast graph representation learning with pytorch geometric"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph convolutional neural networks for web scale recommender systems"
    - "sentence state lstm for text representation"
    - "predicting multicellular function through multi layer tissue networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "graph4code a machine interpretable knowledge graph for code"):
  with members:
    - "improving natural language inference using external knowledge in the science questions domain"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-5 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-6 (induced by source paper "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-7 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "situation recognition with graph neural networks"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"

Connected through hyperedge HE-8 (induced by source paper "hicore visual hierarchical context reasoning"):
  with members:
    - "connection sensitive attention u net for accurate retinal vessel segmentation"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "graph neural networks a review of methods and applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "iterative visual reasoning beyond convolutions"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

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)