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: "active learning for visual question answering an empirical study"
  Abstract: We present an empirical study of active learning for Visual Question Answering, where a deep VQA model selects informative question-image pairs from a pool and queries an oracle for answers to maximally improve its performance under a limited query budget. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a fast and effective goal-driven active learning scoring function to pick question-image pairs for deep VQA models under the Bayesian Neural Network framework. We find that deep VQA models need large amounts of training data before they can start asking informative questions. But once they do, all three approaches outperform the random selection baseline and achieve significant query savings. For the scenario where the model is allowed to ask generic questions about images but is evaluated only on specific questions (e.g., questions whose answer is either yes or no), our proposed goal-driven scoring function performs the best.

Connected through hyperedge HE-1 (induced by source paper "deep bayesian active learning for multiple correct outputs"):
  with members:
    - "why does a visual question have different answers"
    - "bertscore evaluating text generation with bert"
    - "confidence modeling for neural semantic parsing"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "deep active learning for named entity recognition"
    - "learning by asking questions"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "temporal coherence for active learning in videos"):
  with members:
    - "learning loss for active learning"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "unsupervised hard example mining from videos for improved object detection"
    - "active learning for deep object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "the elephant in the room"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-3 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"

Connected through hyperedge HE-4 (induced by source paper "inverse visual question answering a new benchmark and vqa diagnosis tool"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "ivqa inverse visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "the promise of premise harnessing question premises in visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "deep active learning for named entity recognition"
    - "deep bayesian active learning with image data"
    - "active and continuous exploration with deep neural networks and expected model output changes"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "variational bayesian inference with stochastic search"

Connected through hyperedge HE-6 (induced by source paper "being negative but constructively lessons learnt from creating better visual question answering datasets"):
  with members:
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "learned in translation contextualized word vectors"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "leveraging visual question answering for image caption ranking"
    - "analyzing the behavior of visual question answering models"
    - "spice semantic propositional image caption evaluation"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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)