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: "gen cnn a convolutional architecture for word sequence prediction"
  Abstract: We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. Instead, we use a convolutional neural network to predict the next word with the history of words of variable length. Also different from the existing feedforward networks for language modeling, our model can effectively fuse the local correlation and global correlation in the word sequence, with a convolution-gating strategy specifically designed for the task. We argue that our model can give adequate representation of the history, and therefore can naturally exploit both the short and long range dependencies. Our model is fast, easy to train, and readily parallelized. Our extensive experiments on text generation and $n$-best re-ranking in machine translation show that $gen$CNN outperforms the state-of-the-arts with big margins.

Connected through hyperedge HE-1 (induced by source paper "locally smoothed neural networks"):
  with members:
    - "recurrent spatial transformer networks"
    - "spatial transformer networks"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "draw a recurrent neural network for image generation"
    - "recurrent models of visual attention"
    - "multiple object recognition with visual attention"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-2 (induced by source paper "topic augmented neural network for short text conversation"):
  with members:
    - "a persona based neural conversation model"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "text matching as image recognition"
    - "syntax based deep matching of short texts"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-3 (induced by source paper "learning semantically coherent and reusable kernels in convolution neural nets for sentence classification"):
  with members:
    - "multichannel variable size convolution for sentence classification"
    - "attention based convolutional neural network for machine comprehension"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "improved semantic representations from tree structured long short term memory networks"
    - "convolutional neural network architectures for matching natural language sentences"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "discriminative neural sentence modeling by tree based convolution"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "language modeling with gated convolutional networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "factorization tricks for lstm networks"
    - "conditional image generation with pixelcnn decoders"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "exploring the limits of language modeling"
    - "pixel recurrent neural networks"
    - "deep residual learning for image recognition"
    - "strategies for training large vocabulary neural language models"

Connected through hyperedge HE-5 (induced by source paper "character aware neural language models"):
  with members:
    - "character level convolutional networks for text classification"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "probabilistic modelling of morphologically rich languages"
    - "text understanding from scratch"
    - "boosting named entity recognition with neural character embeddings"
    - "training very deep networks"
    - "molding cnns for text non linear non consecutive convolutions"

Connected through hyperedge HE-6 (induced by source paper "neural transformation machine a new architecture for sequence to sequence learning"):
  with members:
    - "towards neural network based reasoning"
    - "effective approaches to attention based neural machine translation"
    - "neural enquirer learning to query tables with natural language"
    - "draw a recurrent neural network for image generation"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"

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.CL(Computation and Language)