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: "solving visual madlibs with multiple cues"
  Abstract: This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset. Instead of generic and commonly used representations trained on the ImageNet dataset, our approach employs a combination of networks trained for specialized tasks such as scene recognition, person activity classification, and attribute prediction. We also present a method for localizing phrases from candidate answers in order to provide spatial support for feature extraction. We map each of these features, together with candidate answers, to a joint embedding space through normalized canonical correlation analysis (CCA). Finally, we solve an optimization problem to learn to combine CCA scores from multiple cues to select the best answer. Extensive experimental results show a significant improvement over the previous state of the art and confirm that answering questions from a wide range of types benefits from examining a variety of image cues and carefully choosing the spatial support of feature extraction.

Connected through hyperedge HE-1 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-2 (induced by source paper "neural sequential phrase grounding seqground"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "a neural multi sequence alignment technique neumatch"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-3 (induced by source paper "attentionrnn a structured spatial attention mechanism"):
  with members:
    - "modular generative adversarial networks"
    - "explainable neural computation via stack neural module networks"
    - "cbam convolutional block attention module"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "visual reference resolution using attention memory for visual dialog"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-4 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

Connected through hyperedge HE-5 (induced by source paper "aligned image word representations improve inductive transfer across vision language tasks"):
  with members:
    - "analyzing the behavior of visual question answering models"
    - "a focused dynamic attention model for visual question answering"
    - "attention correctness in neural image captioning"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning without forgetting"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "conditional image text embedding networks"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"
    - "comprehension guided referring expressions"
    - "deep image retrieval learning global representations for image search"
    - "attention correctness in neural image captioning"
    - "grounding of textual phrases in images by reconstruction"

Connected through hyperedge HE-7 (induced by source paper "the vqa machine learning how to use existing vision algorithms to answer new questions"):
  with members:
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "ask your neurons a deep learning approach to visual question answering"
    - "multimodal residual learning for visual qa"
    - "graph structured representations for visual question answering"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "combining multiple cues for visual madlibs question answering"):
  with members:
    - "dualnet domain invariant network for visual question answering"
    - "the vqa machine learning how to use existing vision algorithms to answer new questions"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"
    - "deep compositional question answering with neural module networks"

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)