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 count objects in natural images for visual question answering"
  Abstract: Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.

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"
    - "deep modular co attention networks for visual question answering"
    - "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"

Connected through hyperedge HE-2 (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"
    - "deep modular co attention networks for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "reciprocal attention fusion 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-3 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "differential attention for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-4 (induced by source paper "bilinear graph networks for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "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-5 (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"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "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-6 (induced by source paper "rsvqa visual question answering for remote sensing data"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "answer them all toward universal visual question answering models"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "dota a large scale dataset for object detection in aerial images"

Connected through hyperedge HE-7 (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"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "answer them all toward universal visual question answering models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "vqd visual query detection in natural scenes"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "reciprocal attention fusion for visual question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visual question answering by bootstrapping hard attention"

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)