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: "generating text with deep reinforcement learning"
  Abstract: We introduce a novel schema for sequence to sequence learning with a Deep Q-Network (DQN), which decodes the output sequence iteratively. The aim here is to enable the decoder to first tackle easier portions of the sequences, and then turn to cope with difficult parts. Specifically, in each iteration, an encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the input sequence, automatically create features to represent the internal states of and formulate a list of potential actions for the DQN. Take rephrasing a natural sentence as an example. This list can contain ranked potential words. Next, the DQN learns to make decision on which action (e.g., word) will be selected from the list to modify the current decoded sequence. The newly modified output sequence is subsequently used as the input to the DQN for the next decoding iteration. In each iteration, we also bias the reinforcement learning's attention to explore sequence portions which are previously difficult to be decoded. For evaluation, the proposed strategy was trained to decode ten thousands natural sentences. Our experiments indicate that, when compared to a left-to-right greedy beam search LSTM decoder, the proposed method performed competitively well when decoding sentences from the training set, but significantly outperformed the baseline when decoding unseen sentences, in terms of BLEU score obtained.

Connected through hyperedge HE-1 (induced by source paper "a multi language platform for generating algebraic mathematical word problems"):
  with members:
    - "neural text generation past present and beyond"
    - "automatic conditional generation of personalized social media short texts"
    - "adversarial text generation via feature mover s distance"
    - "an analysis of neural language modeling at multiple scales"
    - "salsa text self attentive latent space based adversarial text generation"
    - "language gans falling short"
    - "on the state of the art of evaluation in neural language models"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-2 (induced by source paper "human like decision making document level aspect sentiment classification via hierarchical reinforcement learning"):
  with members:
    - "deep learning a critical appraisal"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "aspect level sentiment classification with deep memory network"
    - "rationalizing neural predictions"
    - "adam a method for stochastic optimization"
    - "learning attitudes and attributes from multi aspect reviews"

Connected through hyperedge HE-3 (induced by source paper "the gap of semantic parsing a survey on automatic math word problem solvers"):
  with members:
    - "data driven methods for solving algebra word problems"
    - "semantically aligned equation generation for solving and reasoning math word problems"
    - "mapping to declarative knowledge for word problem solving"
    - "a structured self attentive sentence embedding"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "a diagram is worth a dozen images"
    - "unit dependency graph and its application to arithmetic word problem solving"
    - "a theme rewriting approach for generating algebra word problems"

Connected through hyperedge HE-4 (induced by source paper "salsa text self attentive latent space based adversarial text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "spectral normalization for generative adversarial networks"
    - "towards diverse text generation with inverse reinforcement learning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "texygen a benchmarking platform for text generation models"
    - "character level language modeling with deeper self attention"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "learning how to self learn enhancing self training using neural reinforcement learning"):
  with members:
    - "split and rephrase"
    - "learning how to active learn a deep reinforcement learning approach"
    - "sentence simplification with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "learning to translate in real time with neural machine translation"
    - "predicting the effectiveness of self training application to sentiment classification"
    - "enriching word vectors with subword information"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "correlated discrete data generation using adversarial training"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "adversarially regularized autoencoders"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning from simulated and unsupervised images through adversarial training"
    - "correlational neural networks"
    - "conditional generative adversarial nets"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-7 (induced by source paper "automatic conditional generation of personalized social media short texts"):
  with members:
    - "a persona based neural conversation model"
    - "a neural attention model for abstractive sentence summarization"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "neural machine translation by jointly learning to align and translate"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "a deep network with visual text composition behavior"):
  with members:
    - "toward controlled generation of text"
    - "very deep convolutional networks for natural language processing"
    - "understanding neural networks through representation erasure"
    - "visualizing and understanding neural models in nlp"
    - "improved semantic representations from tree structured long short term memory networks"
    - "training very deep networks"
    - "neural machine translation by jointly learning to align and translate"
    - "visualizing and understanding convolutional networks"

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)