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: "codesum translate program language to natural language"
  Abstract: During software maintenance, programmers spend a lot of time on code comprehension. Reading comments is an effective way for programmers to reduce the reading and navigating time when comprehending source code. Therefore, as a critical task in software engineering, code summarization aims to generate brief natural language descriptions for source code. In this paper, we propose a new code summarization model named CodeSum. CodeSum exploits the attention-based sequence-to-sequence (Seq2Seq) neural network with Structure-based Traversal (SBT) of Abstract Syntax Trees (AST). The AST sequences generated by SBT can better present the structure of ASTs and keep unambiguous. We conduct experiments on three large-scale corpora in different program languages, i.e., Java, C#, and SQL, in which Java corpus is our new proposed industry code extracted from Github. Experimental results show that our method CodeSum outperforms the state-of-the-art significantly.

Connected through hyperedge HE-1 (induced by source paper "towards neural decompilation"):
  with members:
    - "tree to tree neural networks for program translation"
    - "neural attribute machines for program generation"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "six challenges for neural machine translation"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "a convolutional attention network for extreme summarization of source code"
    - "tensorflow a system for large scale machine learning"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "applications of multi view learning approaches for software comprehension"):
  with members:
    - "a survey of machine learning for big code and naturalness"
    - "on the effect of semantically enriched context models on software modularization"
    - "evaluating link prediction methods"
    - "a survey on multi view learning"

Connected through hyperedge HE-3 (induced by source paper "structured neural summarization"):
  with members:
    - "deep learning to detect redundant method comments"
    - "a general path based representation for predicting program properties"
    - "the natural language decathlon multitask learning as question answering"
    - "the adverse effects of code duplication in machine learning models of code"
    - "toward abstractive summarization using semantic representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-4 (induced by source paper "a survey of machine learning for big code and naturalness"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "deep reinforcement learning for programming language correction"
    - "recent advances in neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "autofolding for source code summarization"
    - "neural attribute machines for program generation"
    - "semantically enhanced software traceability using deep learning techniques"

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.SE(Software Engineering)