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 "code generation as a dual task of code summarization"):
  Abstract: Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.

Includes members:
  Member "a grammar based structural cnn decoder for code generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "program synthesis and semantic parsing with learned code idioms")
  Member "dual ask answer network for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review based question generation with adaptive instance transfer and augmentation")
  Member "improving automatic source code summarization via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search")
  Member "dual supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "closed loop matters dual regression networks for single image super resolution")
  Member "a parallel corpus of python functions and documentation strings for automated code documentation and code generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search")
  Member "abstract syntax networks for code generation and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "question answering and question generation as dual tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "a syntactic neural model for general purpose code generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploring neural models for parsing natural language into first order logic")

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.LG(Machine Learning)