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: "question type guided attention in visual question answering"
  Abstract: Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as "Activity Recognition", "Utility" and "Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we achieve 3% improvement for overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack of question type, with minimal performance loss.

Connected through hyperedge HE-1 (induced by source paper "cq vqa visual question answering on categorized questions"):
  with members:
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-2 (induced by source paper "multi modality latent interaction network for visual question answering"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "weakly supervised compositional featureaggregation for few shot recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "2nd place solution to the gqa challenge 2019"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "murel multimodal relational reasoning for visual question answering"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-4 (induced by source paper "compact trilinear interaction for visual question answering"):
  with members:
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-5 (induced by source paper "learning to compose dynamic tree structures for visual contexts"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "context aware visual policy network for sequence level image captioning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "recursive visual attention in visual dialog"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

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)