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: "arabic text recognition in video sequences"
  Abstract: In this paper, we propose a robust approach for text extraction and recognition from Arabic news video sequence. The text included in video sequences is an important needful for indexing and searching system. However, this text is difficult to detect and recognize because of the variability of its size, their low resolution characters and the complexity of the backgrounds. To solve these problems, we propose a system performing in two main tasks: extraction and recognition of text. Our system is tested on a varied database composed of different Arabic news programs and the obtained results are encouraging and show the merits of our approach.

Connected through hyperedge HE-1 (induced by source paper "unconstrained scene text and video text recognition for arabic script"):
  with members:
    - "generating synthetic data for text recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "reading text in the wild with convolutional neural networks"
    - "supervised mid level features for word image representation"
    - "speech recognition with deep recurrent neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning based isolated arabic scene character recognition"):
  with members:
    - "memory matters convolutional recurrent neural network for scene text recognition"
    - "a fine grained approach to scene text script identification"
    - "a cnn based scene chinese text recognition algorithm with synthetic data engine"
    - "efficient scene text localization and recognition with local character refinement"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"

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.MM(Multimedia)