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: "straight to the facts learning knowledge base retrieval for factual visual question answering"
  Abstract: Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.

Connected through hyperedge HE-1 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "structured attention networks"

Connected through hyperedge HE-2 (induced by source paper "ok vqa a visual question answering benchmark requiring external knowledge"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an implementation of faster rcnn with study for region sampling"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "image question answer synergistic network for visual dialog"):
  with members:
    - "bilinear attention networks"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "modulating early visual processing by language"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "learning visual reasoning without strong priors"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-4 (induced by source paper "explainable high order visual question reasoning a new benchmark and knowledge routed network"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "learning visual knowledge memory networks for visual question answering"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-5 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "iqa visual question answering in interactive environments"

Connected through hyperedge HE-6 (induced by source paper "multi modal learning with prior visual relation reasoning"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "stacked cross attention for image text matching"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-7 (induced by source paper "scene graph reasoning with prior visual relationship for visual question answering"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "large scale visual relationship understanding"
    - "compositional attention networks for machine reasoning"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "exploring visual relationship for image captioning"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-8 (induced by source paper "visual question answering as reading comprehension"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "reading wikipedia to answer open domain questions"
    - "squad 100 000 questions for machine comprehension of text"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "revisiting visual question answering baselines"
    - "the vqa machine learning how to use existing vision algorithms to answer new questions"

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)