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: "deep modular co attention networks for visual question answering"
  Abstract: Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63$\%$ overall accuracy on the test-dev set. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "in defense of grid features for visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "unified vision language pre training for image captioning and vqa"
    - "answer them all toward universal visual question answering models"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "quantifying and alleviating the language prior problem in visual question answering"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-3 (induced by source paper "normalized and geometry aware self attention network for image captioning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "multimodal transformer with multi view visual representation for image captioning"
    - "large scale long tailed recognition in an open world"
    - "aligning linguistic words and visual semantic units for image captioning"
    - "context aware self attention networks"

Connected through hyperedge HE-4 (induced by source paper "accuracy vs complexity a trade off in visual question answering models"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "rubi reducing unimodal biases in visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "reciprocal attention fusion for visual question answering"
    - "learning to count objects in natural images for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-5 (induced by source paper "bilinear graph networks for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structure inference net object detection using scene level context and instance level relationships"

Connected through hyperedge HE-6 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "from recognition to cognition visual commonsense reasoning"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-7 (induced by source paper "multi layer content interaction through quaternion product for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "bilinear attention networks"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "learning to count objects in natural images for visual question answering"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "deep contextualized word representations"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"
    - "compositional attention networks for machine 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)