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: "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
  Abstract: Recently, the Visual Question Answering (VQA) task has gained increasing attention in artificial intelligence. Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering. The free-form region based and the detection-based visual attention mechanisms are mostly investigated, with the former ones attending free-form image regions and the latter ones attending pre-specified detection-box regions. We argue that the two attention mechanisms are able to provide complementary information and should be effectively integrated to better solve the VQA problem. In this paper, we propose a novel deep neural network for VQA that integrates both attention mechanisms. Our proposed framework effectively fuses features from free-form image regions, detection boxes, and question representations via a multi-modal multiplicative feature embedding scheme to jointly attend question-related free-form image regions and detection boxes for more accurate question answering. The proposed method is extensively evaluated on two publicly available datasets, COCO-QA and VQA, and outperforms state-of-the-art approaches. Source code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "interactive natural language based person search"):
  with members:
    - "an empirical study of person re identification with attributes"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "identity aware textual visual matching with latent co attention"
    - "person search with natural language description"
    - "adversarial attribute image person re identification"
    - "mask r cnn"
    - "deep attributes driven multi camera person re identification"
    - "vqa visual question answering"

Connected through hyperedge HE-2 (induced by source paper "question agnostic attention for visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "reciprocal attention fusion for visual question answering"
    - "learning to count objects in natural images for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-3 (induced by source paper "deep reason a strong baseline for real world visual reasoning"):
  with members:
    - "towards vqa models that can read"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "learning to compose neural networks for question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-4 (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"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "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 "question guided hybrid convolution for visual question answering"):
  with members:
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "film visual reasoning with a general conditioning layer"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-7 (induced by source paper "reciprocal attention fusion for visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "dual attention networks for multimodal reasoning and matching"
    - "aggregated residual transformations for deep neural networks"
    - "ask your neurons a deep learning approach to visual question answering"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-8 (induced by source paper "r vqa learning visual relation facts with semantic attention for visual question answering"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "mutan multimodal tucker fusion for visual question answering"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "a context aware attention network for interactive question answering"
    - "hadamard product for low rank bilinear pooling"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model 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)