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: "natural language understanding with distributed representation"
  Abstract: This is a lecture note for the course DS-GA 3001   at the Center for Data Science , New York University in Fall, 2015. As the name of the course suggests, this lecture note introduces readers to a neural network based approach to natural language understanding/processing. In order to make it as self-contained as possible, I spend much time on describing basics of machine learning and neural networks, only after which how they are used for natural languages is introduced. On the language front, I almost solely focus on language modelling and machine translation, two of which I personally find most fascinating and most fundamental to natural language understanding.

Connected through hyperedge HE-1 (induced by source paper "stroke constrained attention network for online handwritten mathematical expression recognition"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "compact trilinear interaction for visual question answering"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "attention aware compositional network for person re identification"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-2 (induced by source paper "the unreasonable effectiveness of the forget gate"):
  with members:
    - "can recurrent neural networks warp time"
    - "lstm a search space odyssey"
    - "learning simpler language models with the differential state framework"
    - "dilated recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "minimal gated unit for recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "architectural complexity measures of recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi scale attention with dense encoder for handwritten mathematical expression recognition"):
  with members:
    - "trajectory based radical analysis network for online handwritten chinese character recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "modeling coverage for neural machine translation"
    - "what you get is what you see a visual markup decompiler"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "trajectory based radical analysis network for online handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "denseran for offline handwritten chinese character recognition"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "high performance offline handwritten chinese character recognition using googlenet and directional feature maps"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "translating phrases in neural machine translation"):
  with members:
    - "neural system combination for machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "character based neural machine translation"
    - "pointing the unknown words"
    - "neural machine translation advised by statistical machine translation"
    - "tree to sequence attentional neural machine translation"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-7 (induced by source paper "search engine guided non parametric neural machine translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "predicting target language ccg supertags improves neural machine translation"
    - "billion scale similarity search with gpus"
    - "exploiting cross sentence context for neural machine translation"
    - "neural episodic control"
    - "learning to remember rare events"
    - "towards string to tree neural machine translation"
    - "task oriented query reformulation with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"):
  with members:
    - "modeling coverage for neural machine translation"
    - "what you get is what you see a visual markup decompiler"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "end to end attention based large vocabulary speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "end to end continuous speech recognition using attention based recurrent nn first results"

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)