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: "vqabq visual question answering by basic questions"
  Abstract: Taking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset and yields state-of-the-art accuracy, 60.34% in open-ended task.

Connected through hyperedge HE-1 (induced by source paper "auto classification of retinal diseases in the limit of sparse data using a two streams machine learning model"):
  with members:
    - "a novel hybrid machine learning model for auto classification of retinal diseases"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "a novel framework for robustness analysis of visual qa models"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
    - "robustness analysis of visual qa models by basic questions"
    - "superimposition of eye fundus images for longitudinal analysis from large public health databases"

Connected through hyperedge HE-2 (induced by source paper "a novel hybrid machine learning model for auto classification of retinal diseases"):
  with members:
    - "a novel framework for robustness analysis of visual qa models"
    - "robustness analysis of visual qa models by basic questions"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "aggregated residual transformations for deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "a novel framework for robustness analysis of visual qa models"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "robustness analysis of visual qa models by basic questions"
    - "hadamard product for low rank bilinear pooling"
    - "analyzing the behavior of visual question answering models"
    - "ask your neurons a deep learning approach to visual question answering"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "robustness analysis of visual qa models by basic questions"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "ask your neurons a deep learning approach to visual question answering"
    - "hierarchical question image co attention for visual question answering"
    - "towards evaluating the robustness of neural networks"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "visual7w grounded question answering in images"

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)