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: "learning in the rational speech acts model"
  Abstract: The Rational Speech Acts (RSA) model treats language use as a recursive process in which probabilistic speaker and listener agents reason about each other's intentions to enrich the literal semantics of their language along broadly Gricean lines. RSA has been shown to capture many kinds of conversational implicature, but it has been criticized as an unrealistic model of speakers, and it has so far required the manual specification of a semantic lexicon, preventing its use in natural language processing applications that learn lexical knowledge from data. We address these concerns by showing how to define and optimize a trained statistical classifier that uses the intermediate agents of RSA as hidden layers of representation forming a non-linear activation function. This treatment opens up new application domains and new possibilities for learning effectively from data. We validate the model on a referential expression generation task, showing that the best performance is achieved by incorporating features approximating well-established insights about natural language generation into RSA.

Connected through hyperedge HE-1 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-2 (induced by source paper "pragmatically informative text generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating bilingual pragmatic color references"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "pragmatically informative image captioning with character level reference"
    - "findings of the e2e nlg challenge"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "unified pragmatic models for generating and following instructions"
    - "context aware captions from context agnostic supervision"

Connected through hyperedge HE-3 (induced by source paper "learning autocomplete systems as a communication game"):
  with members:
    - "pythia ai assisted code completion system"
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "learning deep hidden nonlinear dynamics from aggregate data"
    - "unsupervised sentence compression using denoising auto encoders"
    - "planning inference and pragmatics in sequential language games"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating sentences by editing prototypes"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "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"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "planning inference and pragmatics in sequential language games"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "emergence of grounded compositional language in multi agent populations"
    - "a network based end to end trainable task oriented dialogue system"
    - "learning to compose neural networks for question answering"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "multi agent cooperation and the emergence of natural language"

Connected through hyperedge HE-6 (induced by source paper "pragmatically informative image captioning with character level reference"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "generation and comprehension of unambiguous object descriptions"
    - "microsoft coco captions data collection and evaluation server"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-7 (induced by source paper "an incremental iterated response model of pragmatics"):
  with members:
    - "context aware captions from context agnostic supervision"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "reasoning about pragmatics with neural listeners and speakers"

Connected through hyperedge HE-8 (induced by source paper "unified pragmatic models for generating and following instructions"):
  with members:
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "context aware captions from context agnostic supervision"
    - "comprehension guided referring expressions"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "learning language games through interaction"
    - "navigational instruction generation as inverse reinforcement learning with neural machine translation"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "simpler context dependent logical forms via model projections"

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)