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 learning to detect redundant method comments"
  Abstract: Comments in software are critical for maintenance and reuse. But apart from prescriptive advice, there is little practical support or quantitative understanding of what makes a comment useful. In this paper, we introduce the task of identifying comments which are uninformative about the code they are meant to document. To address this problem, we introduce the notion of comment entailment from code, high entailment indicating that a comment's natural language semantics can be inferred directly from the code. Although not all entailed comments are low quality, comments that are too easily inferred, for example, comments that restate the code, are widely discouraged by authorities on software style. Based on this, we develop a tool called CRAIC which scores method-level comments for redundancy. Highly redundant comments can then be expanded or alternately removed by the developer. CRAIC uses deep language models to exploit large software corpora without requiring expensive manual annotations of entailment. We show that CRAIC can perform the comment entailment task with good agreement with human judgements. Our findings also have implications for documentation tools. For example, we find that common tags in Javadoc are at least two times more predictable from code than non-Javadoc sentences, suggesting that Javadoc tags are less informative than more free-form comments

Connected through hyperedge HE-1 (induced by source paper "pre trained contextual embedding of source code"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "neural program repair by jointly learning to localize and repair"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "when deep learning met code search"
    - "roberta a robustly optimized bert pretraining approach"
    - "code2vec learning distributed representations of code"
    - "self attention with relative position representations"
    - "the adverse effects of code duplication in machine learning models of code"

Connected through hyperedge HE-2 (induced by source paper "a neural model for generating natural language summaries of program subroutines"):
  with members:
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "attention is all you need"
    - "the code2text challenge text generation in source libraries"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "a neural architecture for generating natural language descriptions from source code changes"

Connected through hyperedge HE-3 (induced by source paper "structured neural summarization"):
  with members:
    - "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"
    - "code2seq generating sequences from structured representations of 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.SE(Software Engineering)