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: "individual and domain adaptation in sentence planning for dialogue"
  Abstract: One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations.

Connected through hyperedge HE-1 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability"):
  with members:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-2 (induced by source paper "constrained decoding for neural nlg from compositional representations in task oriented dialogue"):
  with members:
    - "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"
    - "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"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "findings of the e2e nlg challenge"

Connected through hyperedge HE-3 (induced by source paper "tree structured semantic encoder with knowledge sharing for domain adaptation in natural language generation"):
  with members:
    - "variational cross domain natural language generation for spoken dialogue systems"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "the e2e dataset new challenges for end to end generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-4 (induced by source paper "improving quality and efficiency in plan based neural data to text generation"):
  with members:
    - "revisiting adversarial autoencoder for unsupervised word translation with cycle consistency and improved training"
    - "step by step separating planning from realization in neural data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "neuralreg an end to end approach to referring expression generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "end to end content and plan selection for data to text generation"):
  with members:
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "unsupervised natural language generation with denoising autoencoders"
    - "findings of the e2e nlg challenge"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "get to the point summarization with pointer generator networks"
    - "addressing the data sparsity issue in neural amr parsing"

Connected through hyperedge HE-6 (induced by source paper "adversarial domain adaptation for variational neural language generation in dialogue systems"):
  with members:
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "latent intention dialogue models"
    - "adversarial discriminative domain adaptation"
    - "semantic refinement gru based neural language generation for spoken dialogue systems"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-7 (induced by source paper "rankme reliable human ratings for natural language generation"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "challenges in data to document generation"
    - "the e2e dataset new challenges for end to end generation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "natural language generation enhances human decision making with uncertain information"

Connected through hyperedge HE-8 (induced by source paper "can neural generators for dialogue learn sentence planning and discourse structuring"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "neural multivoice models for expressing novel personalities in dialog"
    - "learning neural templates for text generation"
    - "polite dialogue generation without parallel data"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "controlling linguistic style aspects in neural language generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response 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)