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 "typewriter neural type prediction with search based validation"):
  Abstract: Maintaining large code bases written in dynamically typed languages, such as JavaScript or Python, can be challenging: simple data compatibility errors proliferate, IDE support is lacking and APIs are harder to comprehend. Recent work attempts to address those issues through either static analysis or probabilistic type inference. Unfortunately, static type inference for dynamic languages is inherently limited, while probabilistic approaches suffer from imprecision. This paper presents TypeWriter, the first combination of probabilistic prediction with search-based refinement of predicted types. TypeWriter's predictor learns to infer the return and argument types for functions from partially annotated code bases by combining the natural language properties of code with programming language-level information. To validate predicted types, TypeWriter invokes a gradual type checker with different combinations of the predicted types, while navigating the space of possible type combinations in a feedback-directed manner. We implement the TypeWriter approach for Python and evaluate it on two code corpora: a multi-million line code base at Facebook and a collection of 500 popular open-source projects. We show that TypeWriter's type predictor achieves a precision of 64% (91%) and a recall of 52% (68%) in the top-1 (top-5) predictions, and demonstrate that usage contexts are a helpful addition to neural type predictors. By combining predictions with search-based validation, TypeWriter can fully annotate between 42% to 64% of the files in a randomly selected corpus, while ensuring type correctness. A comparison with a static type inference tool shows that TypeWriter adds many more non-trivial types. Overall, TypeWriter provides developers with an effective way to help with the transition to fully type-annotated code.

Includes members:
  Member "on learning meaningful assert statements for unit test cases":
    also connected through other hyperedges:
      (none)
  Member "big code big vocabulary open vocabulary models for source code":
    also connected through other hyperedges:
      (none)
  Member "modeling vocabulary for big code machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "on learning meaningful code changes via neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "maybe deep neural networks are the best choice for modeling source code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "getafix learning to fix bugs automatically":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial examples for models of code")
  Member "learning to represent edits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structural language models of code")
  Member "neural networks for modeling source code edits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural language models for any code generation")

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)