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: "learning natural coding conventions"
  Abstract: Every programmer has a characteristic style, ranging from preferences about identifier naming to preferences about object relationships and design patterns. Coding conventions define a consistent syntactic style, fostering readability and hence maintainability. When collaborating, programmers strive to obey a project’s coding conventions. However, one third of reviews of changes contain feedback about coding conventions, indicating that programmers do not always follow them and that project members care deeply about adherence. Unfortunately, programmers are often unaware of coding conventions because inferring them requires a global view, one that aggregates the many local decisions programmers make and identifies emergent consensus on style. We present NATURALIZE, a framework that learns the style of a codebase, and suggests revisions to improve stylistic consistency. NATURALIZE builds on recent work in applying statistical natural language processing to source code. We apply NATURALIZE to suggest natural identifier names and formatting conventions. We present four tools focused on ensuring natural code during development and release management, including code review. NATURALIZE achieves 94 % accuracy in its top suggestions for identifier names. We used NATURALIZE to generate 18 patches for 5 open source projects: 14 were accepted.

Connected through hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "the adverse effects of code duplication in machine learning models of code"
    - "frage frequency agnostic word representation"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "learning to represent programs with property signatures"):
  with members:
    - "learning to infer program sketches"
    - "synthetic datasets for neural program synthesis"
    - "write execute assess program synthesis with a repl"
    - "code2vec learning distributed representations of code"
    - "the three pillars of machine programming"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-3 (induced by source paper "authorship attribution of source code a language agnostic approach and applicability in software engineering"):
  with members:
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "usage and attribution of stack overflow code snippets in github projects"
    - "code2seq generating sequences from structured representations of code"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "maybe deep neural networks are the best choice for modeling source code"):
  with members:
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-6 (induced by source paper "ir2vec a flow analysis based scalable infrastructure for program encodings"):
  with members:
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "a survey of machine learning for big code and naturalness"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "source forager a search engine for similar source code"

Connected through hyperedge HE-7 (induced by source paper "dire a neural approach to decompiled identifier naming"):
  with members:
    - "neural reverse engineering of stripped binaries"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "code2seq generating sequences from structured representations of code"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "neural message passing for quantum chemistry"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-8 (induced by source paper "do people prefer natural code"):
  with members:
    - "automatically generating documentation for lambda expressions in java"
    - "replication can improve prior results a github study of pull request acceptance"
    - "the adverse effects of code duplication in machine learning models of code"
    - "categorizing the content of github readme files"
    - "studying the difference between natural and programming language corpora"
    - "attention is all you need"
    - "a survey of machine learning for big code and naturalness"
    - "on the naturalness of buggy 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)