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 "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.

Includes members:
  Member "smartpaste learning to adapt source code":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepbugs a learning approach to name based bug detection")
  Member "neural programming by example":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finite biased teaching with infinite concept classes")
  Member "learn fuzz machine learning for input fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "automated correction for syntax errors in programming assignments using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "deepcoder learning to write programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "semi supervised sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "learning natural coding conventions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "big code big vocabulary open vocabulary models for source code")

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.SE(Software Engineering)