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: "memexqa visual memex question answering"
  Abstract: This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection. Towards solving the task, we 1) present the MemexQA dataset, a large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network architecture for image, text and video question answering. Experimental results on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines and yields the state-of-the-art on this novel and challenging task. The promising results on TextQA and VideoQA suggest MemexNet's efficacy and scalability across various QA tasks.

Connected through hyperedge HE-1 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "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"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-2 (induced by source paper "towards vqa models that can read"):
  with members:
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "disentangling language and knowledge in task oriented dialogs"
    - "rosetta large scale system for text detection and recognition in images"
    - "dvqa understanding data visualizations via question answering"
    - "figureqa an annotated figure dataset for visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "progressive attention memory network for movie story question answering"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "multimodal dual attention memory for video story question answering"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "mutan multimodal tucker fusion for visual question answering"
    - "attention is all you need"
    - "tgif qa toward spatio temporal reasoning in visual question answering"

Connected through hyperedge HE-4 (induced by source paper "visual question answering using deep learning a survey and performance analysis"):
  with members:
    - "tallyqa answering complex counting questions"
    - "differential attention for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "an analysis of visual question answering algorithms"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"

Connected through hyperedge HE-5 (induced by source paper "technical report of the video event reconstruction and analysis vera system shooter localization models interface and beyond"):
  with members:
    - "focal visual text attention for memex question answering"
    - "peeking into the future predicting future person activities and locations in videos"
    - "multimodal co training for selecting good examples from webly labeled video"
    - "exploiting multi modal curriculum in noisy web data for large scale concept learning"

Connected through hyperedge HE-6 (induced by source paper "holistic multi modal memory network for movie question answering"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "attention on attention architectures for visual question answering vqa"
    - "tvqa localized compositional video question answering"
    - "decoupled novel object captioner"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "learning two branch neural networks for image text matching tasks"

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)