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: "what to talk about and how selective generation using lstms with coarse to fine alignment"
  Abstract: We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small subset of salient records to talk about, and finally employs a decoder to generate free-form descriptions of the aligned, selected records. Our model achieves the best selection and generation results reported to-date (with 59% relative improvement in generation) on the benchmark WeatherGov dataset, despite using no specialized features or linguistic resources. Using an improved k-nearest neighbor beam filter helps further. We also perform a series of ablations and visualizations to elucidate the contributions of our key model components. Lastly, we evaluate the generalizability of our model on the RoboCup dataset, and get results that are competitive with or better than the state-of-the-art, despite being severely data-starved.

Connected through hyperedge HE-1 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "using monolingual data in neural machine translation a systematic study"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"

Connected through hyperedge HE-2 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "findings of the e2e nlg challenge"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "order planning neural text generation from structured data"
    - "the e2e dataset new challenges for end to end generation"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-3 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-5 (induced by source paper "survey on evaluation methods for dialogue systems"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "coqa a conversational question answering challenge"
    - "topic based evaluation for conversational bots"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "quac question answering in context"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-6 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  with members:
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "opennmt neural machine translation toolkit"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-7 (induced by source paper "long and diverse text generation with planning based hierarchical variational model"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "step by step separating planning from realization in neural data to text generation"
    - "towards generating long and coherent text with multi level latent variable models"
    - "a hierarchical latent structure for variational conversation modeling"
    - "texygen a benchmarking platform for text generation models"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "generating high quality and informative conversation responses with sequence to sequence models"

Connected through hyperedge HE-8 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "argument generation with retrieval planning and realization"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "hierarchical neural story generation"

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)