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: "context2name a deep learning based approach to infer natural variable names from usage contexts"
  Abstract: Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep learningbased technique that partially reverses the effect of minification by predicting natural identifier names for minified names. The core idea is to predict from the usage context of a variable a name that captures the meaning of the variable. The approach combines a lightweight, token-based static analysis with an auto-encoder neural network that summarizes usage contexts and a recurrent neural network that predict natural names for a given usage context. We evaluate Context2Name with a large corpus of real-world JavaScript code and show that it successfully predicts 47.5% of all minified identifiers while taking only 2.9 milliseconds on average to predict a name. A comparison with the state-of-the-art tools JSNice and JSNaughty shows that our approach performs comparably in terms of accuracy while improving in terms of efficiency. Moreover, Context2Name complements the state-of-the-art by predicting 5.3% additional identifiers that are missed by both existing tools.

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 "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "opennmt open source toolkit for neural machine translation"
    - "neural network based graph embedding for cross platform binary code similarity detection"

Connected through hyperedge HE-3 (induced by source paper "in nomine function naming functions in stripped binaries with neural networks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "dire a neural approach to decompiled identifier naming"
    - "neural reverse engineering of stripped binaries"
    - "tree2tree neural translation model for learning source code changes"
    - "universal language model fine tuning for text classification"
    - "a deep tree based model for software defect prediction"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"

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 "adversarial examples for models of code"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "when deep learning met code search"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "targeted adversarial examples for black box audio systems"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-6 (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"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-7 (induced by source paper "neural reverse engineering of stripped binaries"):
  with members:
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "mapping language to code in programmatic context"
    - "generative code modeling with graphs"
    - "tree to tree neural networks for program translation"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-8 (induced by source paper "neural bug finding a study of opportunities and challenges"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "neural program repair by jointly learning to localize and repair"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "learning to represent edits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "generative code modeling with graphs"
    - "path based function embedding and its application to specification mining"

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)