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: "dropout improves recurrent neural networks for handwriting recognition"
  Abstract: Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequence is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.

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"
    - "implicit language model in lstm for ocr"

Connected through hyperedge HE-2 (induced by source paper "dropout explicit forms and capacity control"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "on the implicit bias of dropout"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "brain tumor segmentation with deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-3 (induced by source paper "understanding lstm a tutorial into long short term memory recurrent neural networks"):
  with members:
    - "listen attend and spell"
    - "grid long short term memory"
    - "attention based models for speech recognition"
    - "a neural conversational model"
    - "neural turing machines"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a clockwork rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "label organized memory augmented neural network"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "neural word search in historical manuscript collections"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "information theoretical label embeddings for large scale image classification"
    - "identity mappings in deep residual networks"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"

Connected through hyperedge HE-6 (induced by source paper "candidate fusion integrating language modelling into a sequence to sequence handwritten word recognition architecture"):
  with members:
    - "are 2d lstm really dead for offline text recognition"
    - "confidence prediction for lexicon free ocr"
    - "attention is all you need"
    - "carla an open urban driving simulator"
    - "generating synthetic data for text recognition"
    - "a critical review of recurrent neural networks for sequence learning"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "interpreting recurrent neural networks behaviour via excitable network attractors"):
  with members:
    - "full force a target based method for training recurrent networks"
    - "can recurrent neural networks warp time"
    - "methods for interpreting and understanding deep neural networks"
    - "training echo state networks with regularization through dimensionality reduction"
    - "orthogonal echo state networks and stochastic evaluations of likelihoods"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "an overview of gradient descent optimization algorithms"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "chd consecutive horizontal dropout for human gait feature extraction"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "horizontal pyramid matching for person re identification"
    - "gaitset regarding gait as a set for cross view gait recognition"
    - "in defense of the triplet loss for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "improved dropout for shallow and deep learning"
    - "towards dropout training for convolutional neural networks"
    - "deep residual learning for image 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)