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: "dynamic graph generation network generating relational knowledge from diagrams"
  Abstract: In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications.

Connected through hyperedge HE-1 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"

Connected through hyperedge HE-2 (induced by source paper "engn a high throughput and energy efficient accelerator for large graph neural networks"):
  with members:
    - "bandwidth reduction using importance weighted pruning on ring allreduce"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "recent advances in recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "classifying diagrams and their parts using graph neural networks a comparison of crowd sourced and expert annotations"):
  with members:
    - "understanding categorizing and predicting semantic image text relations"
    - "cite a corpus of image text discourse relations"
    - "simplifying graph convolutional networks"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "pitfalls of graph neural network evaluation"
    - "tune a research platform for distributed model selection and training"
    - "a diagram is worth a dozen images"

Connected through hyperedge HE-4 (induced by source paper "ai2d rst a multimodal corpus of 1000 primary school science diagrams"):
  with members:
    - "classifying diagrams and their parts using graph neural networks a comparison of crowd sourced and expert annotations"
    - "a comprehensive survey on graph neural networks"
    - "a diagram is worth a dozen images"

Connected through hyperedge HE-5 (induced by source paper "textbook question answering with knowledge graph understanding and unsupervised open set text comprehension"):
  with members:
    - "learning conditioned graph structures for interpretable visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "bidirectional attention flow for machine comprehension"
    - "a diagram is worth a dozen images"
    - "squad 100 000 questions for machine comprehension of text"
    - "graph structured representations for visual question answering"
    - "ms marco a human generated machine reading comprehension dataset"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "textbook question answering with multi modal context graph understanding and self supervised open set comprehension"):
  with members:
    - "bidirectional attention flow for machine comprehension"
    - "a diagram is worth a dozen images"
    - "squad 100 000 questions for machine comprehension of text"
    - "graph structured representations for visual question answering"
    - "ms marco a human generated machine reading comprehension dataset"
    - "semi supervised classification with graph convolutional networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"

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)