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: "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
  Abstract: We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach. We were able to train both setups successfully using very little training data. The joint setup offers better performance, surpassing state-of-the-art with regards to n-gram-based scores while providing more relevant outputs.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (induced by source paper "how to build user simulators to train rl based dialog systems"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "unifying human and statistical evaluation for natural language generation"
    - "unsupervised dialog structure learning"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "building a conversational agent overnight with dialogue self play"
    - "sentiment adaptive end to end dialog systems"

Connected through hyperedge HE-5 (induced by source paper "copy mechanism and tailored training for character based data to text generation"):
  with members:
    - "character level language modeling with deeper self attention"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "neural machine translation of rare words with subword units"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "neural data to text generation a comparison between pipeline and end to end architectures"):
  with members:
    - "designing a symbolic intermediate representation for neural surface realization"
    - "step by step separating planning from realization in neural data to text generation"
    - "neuralreg an end to end approach to referring expression generation"
    - "end to end content and plan selection for data to text generation"
    - "deep graph convolutional encoders for structured data to text generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "the e2e dataset new challenges for end to end generation"

Connected through hyperedge HE-7 (induced by source paper "semantic noise matters for neural natural language generation"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "object hallucination in image captioning"
    - "findings of the e2e nlg challenge"
    - "challenges in data to document generation"
    - "six challenges for neural machine translation"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "a good sample is hard to find noise injection sampling and self training for neural language generation models"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "pragmatically informative text generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "noisy parallel approximate decoding for conditional recurrent language model"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "learning in the rational speech acts model"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

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)