human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  Abstract: Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and program-ming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL andPython, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.

Includes members:
  Member "coacor code annotation for code retrieval with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search")
  Member "dual supervised learning for natural language understanding and generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "jointly learning semantic parser and natural language generator via dual information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction")
  Member "same representation different attentions shareable sentence representation learning from multiple tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report")
  Member "staqc a systematically mined question code dataset from stack overflow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph4code a machine interpretable knowledge graph for code")
  Member "improving automatic source code summarization via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search")
  Member "code2seq generating sequences from structured representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "dual supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "closed loop matters dual regression networks for single image super resolution")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)