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: "backward and forward language modeling for constrained sentence generation"
  Abstract: Recent language models, especially those based on recurrent neural networks (RNNs), make it possible to generate natural language from a learned probability. Language generation has wide applications including machine translation, summarization, question answering, conversation systems, etc. Existing methods typically learn a joint probability of words conditioned on additional information, which is (either statically or dynamically) fed to RNN's hidden layer. In many applications, we are likely to impose hard constraints on the generated texts, i.e., a particular word must appear in the sentence. Unfortunately, existing approaches could not solve this problem. In this paper, we propose a novel backward and forward language model. Provided a specific word, we use RNNs to generate previous words and future words, either simultaneously or asynchronously, resulting in two model variants. In this way, the given word could appear at any position in the sentence. Experimental results show that the generated texts are comparable to sequential LMs in quality.

Connected through hyperedge HE-1 (induced by source paper "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"):
  with members:
    - "neural text generation past present and beyond"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "texygen a benchmarking platform for text generation models"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "cgmh constrained sentence generation by metropolis hastings sampling"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-3 (induced by source paper "a multi modal chinese poetry generation model"):
  with members:
    - "flexible and creative chinese poetry generation using neural memory"
    - "chinese song iambics generation with neural attention based model"
    - "can machine generate traditional chinese poetry a feigenbaum test"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "hierarchical question image co attention for visual question answering"
    - "generating chinese classical poems with rnn encoder decoder"
    - "chinese poetry generation with planning based neural network"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-4 (induced by source paper "incorporating discriminator in sentence generation a gibbs sampling method"):
  with members:
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "language as a latent variable discrete generative models for sentence compression"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-5 (induced by source paper "a constrained sequence to sequence neural model for sentence simplification"):
  with members:
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "sequence to backward and forward sequences a content introducing approach to generative short text conversation"):
  with members:
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "chinese poetry generation with planning based neural network"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "neural generative question answering"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-7 (induced by source paper "compressing neural language models by sparse word representations"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "a neural attention model for abstractive sentence summarization"
    - "distilling word embeddings an encoding approach"
    - "strategies for training large vocabulary neural language models"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "visualizing and understanding recurrent networks"
    - "sparse overcomplete word vector representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "dialogue session segmentation by embedding enhanced texttiling"):
  with members:
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "natural language inference by tree based convolution and heuristic matching"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "topic segmentation and labeling in asynchronous conversations"

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)