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: "cohort of lstm and lexicon verification for handwriting recognition with gigantic lexicon"
  Abstract: Handwriting recognition state of the art methods are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN) coupled with the use of linguistic knowledge. LSTM RNN presents high raw performance and interesting training properties that allow us to break with the standard method at the state of the art. We present a simple and efficient way to extract from a single training a large number of complementary LSTM RNN, called cohort, combined in a cascade architecture with a lexical verification. This process does not require fine tuning, making it easy to use. Our verification allow to deal quickly and efficiently with gigantic lexicon (over 3 million words). We achieve state of the art results for isolated word recognition with very large lexicon and present novel results for an unprecedented gigantic lexicon.

Connected through hyperedge HE-1 (induced by source paper "deep neural networks regularization for structured output prediction"):
  with members:
    - "fcns in the wild pixel level adversarial and constraint based adaptation"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "structured prediction energy networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "deep structured output learning for unconstrained text recognition"
    - "deep visual semantic alignments for generating image descriptions"
    - "sequence to sequence learning with neural networks"

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)