human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visual text correction"):
  Abstract: This paper introduces a new problem, called Visual Text Correction (VTC), i.e., finding and replacing an inaccurate word in the textual description of a video. We propose a deep network that can simultaneously detect an inaccuracy in a sentence, and fix it by replacing the inaccurate word(s). Our method leverages the semantic interdependence of videos and words, as well as the short-term and long-term relations of the words in a sentence. Our proposed formulation can solve the VTC problem employing an End-to-End network in two steps: (1) Inaccuracy detection, and (2) correct word prediction. In detection step, each word of a sentence is reconstructed such that the reconstruction for the inaccurate word is maximized. We exploit both Short Term and Long Term Dependencies employing respectively Convolutional N-Grams and LSTMs to reconstruct the word vectors. For the correction step, the basic idea is to simply substitute the word with the maximum reconstruction error for a better one. The second step is essentially a classification problem where the classes are the words in the dictionary as replacement options. Furthermore, to train and evaluate our model, we propose an approach to automatically construct a large dataset for the VTC problem. Our experiments and performance analysis demonstrates that the proposed method provides very good results and also highlights the general challenges in solving the VTC problem. To the best of our knowledge, this work is the first of its kind for the Visual Text Correction task.

Includes members:
  Member "video fill in the blank using lr rl lstms with spatial temporal attentions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "a dataset and exploration of models for understanding video data through fill in the blank question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tvqa spatio temporal grounding for video question answering")
  Member "language modeling with gated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual lensing of universal sentence representations")
  Member "end to end concept word detection for video captioning retrieval and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips")
  Member "dynamic memory networks for visual and textual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "exploring models and data for image question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "vqa visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")

Question: Please tell me which class the source paper 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)