human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "image grounded conversations multimodal context for natural question and response generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "the promise of premise harnessing question premises in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gqa a new dataset for real world visual reasoning and compositional question answering")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action for better prediction")
  Member "systematic evaluation of convolution neural network advances on the imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "end to end optimization of goal driven and visually grounded dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised question decomposition for question answering")
  Member "leveraging visual question answering for image caption ranking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multimodal image text embeddings for automatic cross media retrieval")
  Member "guesswhat visual object discovery through multi modal dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")

Question: Please tell me which class the source paper 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)