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: "deep reinforcement learning for sequence to sequence models"
  Abstract: In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide state-of-the-art performance in a wide variety of tasks such as machine translation, headline generation, text summarization, speech to text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder-decoder models produce competitive results, many researchers have proposed additional improvements over these sequence-to-sequence models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories. We present some of the most recent frameworks that combine concepts from RL and deep neural networks and explain how these two areas could benefit from each other in solving complex seq2seq tasks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization.

Connected through hyperedge HE-1 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "ernie enhanced representation through knowledge integration"
    - "a neural model for generating natural language summaries of program subroutines"
    - "characterizing and detecting cuda program bugs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "aroma code recommendation via structural code search"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "active inductive logic programming for code search"

Connected through hyperedge HE-2 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "autoregressive text generation beyond feedback loops"
    - "neural text generation past present and beyond"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"
    - "deep state space models for unconditional word generation"

Connected through hyperedge HE-3 (induced by source paper "deep learning for time series forecasting the electric load case"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "comparative study of cnn and rnn for natural language processing"
    - "pixel recursive super resolution"
    - "convolutional sequence to sequence learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "this email could save your life introducing the task of email subject line generation"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "metric for automatic machine translation evaluation based on universal sentence representations"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "sentence simplification with deep reinforcement learning"
    - "actionable email intent modeling with reparametrized rnns"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "keyphrase generation a multi aspect survey"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "neural keyphrase generation via reinforcement learning with adaptive rewards"
    - "a review of keyphrase extraction"
    - "keyphrase generation a text summarization struggle"
    - "efficiency metrics for data driven models a text summarization case study"
    - "keyphrase generation with correlation constraints"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "semi supervised learning for neural keyphrase generation"

Connected through hyperedge HE-6 (induced by source paper "an empirical comparison on imitation learning and reinforcement learning for paraphrase generation"):
  with members:
    - "learning semantic sentence embeddings using pair wise discriminator"
    - "the natural language decathlon multitask learning as question answering"
    - "a study of reinforcement learning for neural machine translation"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a continuously growing dataset of sentential paraphrases"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-7 (induced by source paper "leafnats an open source toolkit and live demo system for neural abstractive text summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "xnmt the extensible neural machine translation toolkit"
    - "bottom up abstractive summarization"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-8 (induced by source paper "a survey of automatic generation of source code comments algorithms and techniques"):
  with members:
    - "automatic generation of text descriptive comments for code blocks"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "code attention translating code to comments by exploiting domain features"
    - "a survey of machine learning for big code and naturalness"
    - "convolutional sequence to sequence learning"
    - "a convolutional attention network for extreme summarization of source code"

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.LG(Machine Learning)