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: "cells in multidimensional recurrent neural networks"
  Abstract: The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi-dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen greater than one. We defined some useful and necessary properties for the one-dimensional LSTM cell and extend them in the multi-dimensional case. Thereby we introduce several new cells with better stability. We present a method to design cells using the theory of linear shift invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT and Rimes database, where we can improve the recognition rate compared to the LSTM cell. So each application where the LSTM cells in MDRNNs are used could be improved by substituting them by the new developed cells.

Connected through hyperedge HE-1 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "regular expressions for decoding of neural network outputs"

Connected through hyperedge HE-2 (induced by source paper "no padding please efficient neural handwriting recognition"):
  with members:
    - "joint recognition of handwritten text and named entities with a neural end to end model"
    - "stronger baselines for trustable results in neural machine translation"
    - "pixel recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "on the difficulty of training recurrent neural networks"
    - "multi dimensional recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "returnn the rwth extensible training framework for universal recurrent neural networks"):
  with members:
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "associative long short term memory"
    - "comparative study of deep learning software frameworks"
    - "blocks and fuel frameworks for deep learning"
    - "comparative study of caffe neon theano and torch for deep learning"
    - "gated feedback recurrent neural networks"
    - "automatic differentiation in machine learning a survey"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

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.AI(Artificial Intelligence)