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: "robustness analysis of visual qa models by basic questions"
  Abstract: Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. 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 ranked basic questions, with similarity scores, 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 about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.

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"
    - "vqabq visual question answering 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"
    - "vqabq visual question answering 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"
    - "vqabq visual question answering 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"

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)