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: "learning to disambiguate by asking discriminative questions"
  Abstract: The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of generating discriminative questions to help disambiguate visual instances. Our work can be seen as a complement and new extension to the rich research studies on image captioning and question answering. We introduce the first large-scale dataset with over 10,000 carefully annotated images-question tuples to facilitate benchmarking. In particular, each tuple consists of a pair of images and 4.6 discriminative questions (as positive samples) and 5.9 non-discriminative questions (as negative samples) on average. In addition, we present an effective method for visual discriminative question generation. The method can be trained in a weakly supervised manner without discriminative images-question tuples but just existing visual question answering datasets. Promising results are shown against representative baselines through quantitative evaluations and user studies.

Connected through hyperedge HE-1 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"

Connected through hyperedge HE-2 (induced by source paper "playing log n questions over sentences"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to describe differences between pairs of similar images"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "interpretable semantic textual similarity"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "end to end memory networks"
    - "a large annotated corpus for learning natural language inference"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-3 (induced by source paper "a corpus for reasoning about natural language grounded in photographs"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "visual entailment a novel task for fine grained image understanding"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "using syntax to ground referring expressions in natural images"
    - "learning visual question answering by bootstrapping hard attention"
    - "talk the walk navigating new york city through grounded dialogue"

Connected through hyperedge HE-4 (induced by source paper "discriminability objective for training descriptive captions"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "learning two branch neural networks for image text matching tasks"
    - "actor critic sequence training for image captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "context aware captions from context agnostic supervision"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-5 (induced by source paper "semantic bottleneck for computer vision tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpreting cnns via decision trees"
    - "explanation methods in deep learning users values concerns and challenges"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "a survey of methods for explaining black box models"

Connected through hyperedge HE-6 (induced by source paper "visual question generation for class acquisition of unknown objects"):
  with members:
    - "creativity generating diverse questions using variational autoencoders"
    - "poincar e embeddings for learning hierarchical representations"
    - "generating natural questions about an image"
    - "automatic generation of grounded visual questions"
    - "towards open set deep networks"
    - "you only look once unified real time object detection"
    - "towards open world recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "answering visual relational queries in web extracted knowledge graphs"):
  with members:
    - "embedding multimodal relational data for knowledge base completion"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "deep metric learning with angular loss"
    - "a neural knowledge language model"
    - "knowledge base completion baselines strike back"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "deep metric learning via facility location"
    - "graph structured representations for visual question answering"

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)