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: "high order attention models for visual question answering"
  Abstract: The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "information maximizing visual question generation"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to compose dynamic tree structures for visual contexts"

Connected through hyperedge HE-4 (induced by source paper "from two graphs to n questions a vqa dataset for compositional reasoning on vision and commonsense"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tallyqa answering complex counting questions"
    - "relational inductive biases deep learning and graph networks"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "vip cnn visual phrase guided convolutional neural network"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "non monotonic logical reasoning guiding deep learning for explainable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning conditioned graph structures for interpretable visual question answering"

Connected through hyperedge HE-6 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"
    - "image captioning and visual question answering based on attributes and their related external knowledge"

Connected through hyperedge HE-8 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "iqa visual question answering in interactive environments"

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)