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: "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
  Abstract: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multi-modal feature fusion that is able to capture the complex interactions between multi-modal features; 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multi-modal feature fusion, a generalized Multi-modal Factorized High-order pooling approach (MFH) is developed to achieve more effective fusion of multi-modal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the KL (Kullback-Leibler) divergence is used as the loss function to achieve more accurate characterization of the complex correlations between multiple diverse answers with same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A deep neural network architecture is designed to integrate all these aforementioned modules into one unified model for achieving superior VQA performance. With an ensemble of 9 models, we achieve the state-of-the-art performance on the large-scale VQA datasets and win the runner-up in the VQA Challenge 2017.

Connected through hyperedge HE-1 (induced by source paper "vrr vg refocusing visually relevant relationships"):
  with members:
    - "graph r cnn for scene graph generation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "large scale visual relationship understanding"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "exploring visual relationship for image captioning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-2 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-3 (induced by source paper "the resale price prediction of secondhand jewelry items using a multi modal deep model with iterative co attention"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "murel multimodal relational reasoning for visual question answering"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "mutan multimodal tucker fusion for visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-4 (induced by source paper "dual recurrent attention units for visual question answering"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "learning answer embeddings for visual question answering"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "a simple loss function for improving the convergence and accuracy of visual question answering models"
    - "survey of visual question answering datasets and techniques"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning to count objects in natural images for visual question answering"):
  with members:
    - "structured attention networks"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "learning detection with diverse proposals"
    - "count ception counting by fully convolutional redundant counting"
    - "a simple neural network module for relational reasoning"
    - "learning non maximum suppression"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "structured attentions for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "interpretable visual question answering by visual grounding from attention supervision mining"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "exploring human like attention supervision in visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "hierarchical question image co attention for visual question answering"
    - "visual question answering a survey of methods and datasets"

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)