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: "implicit language model in lstm for ocr"
  Abstract: Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown. One key difference between current neural network techniques using LSTMs and the previous state-of-the-art HMM systems is that HMM systems have a strong independence assumption. In comparison LSTMs have no explicit constraints on the amount of context that can be considered during decoding. In this paper we show that they learn an implicit LM and attempt to characterize the strength of the LM in terms of equivalent n-gram context. We show that this implicitly learned language model provides a 2.4% CER improvement on our synthetic test set when compared against a test set of random characters (i.e. not naturally occurring sequences), and that the LSTM learns to use up to 5 characters of context (which is roughly 88 frames in our configuration). We believe that this is the first ever attempt at characterizing the strength of the implicit LM in LSTM based OCR systems.

Connected through hyperedge HE-1 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "memory augmented neural networks for predictive process analytics"
    - "robust and scalable differentiable neural computer for question answering"
    - "rosetta large scale system for text detection and recognition in images"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "reading scene text with attention convolutional sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "efficient lexicon free ocr using deep learning"):
  with members:
    - "rosetta large scale system for text detection and recognition in images"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "squeeze and excitation networks"
    - "tensorflow a system for large scale machine learning"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "field typing for improved recognition on heterogeneous handwritten forms"):
  with members:
    - "full page text recognition learning where to start and when to stop"
    - "detecting oriented text in natural images by linking segments"
    - "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"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "deep structured output learning for unconstrained text recognition"

Connected through hyperedge HE-4 (induced by source paper "are 2d lstm really dead for offline text recognition"):
  with members:
    - "rosetta large scale system for text detection and recognition in images"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "dropout improves recurrent neural networks for handwriting recognition"

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)