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 syntactic program transformations from examples"
  Abstract: IDEs, such as Visual Studio, automate common transformations, such as Rename and Extract Method refactorings. However, extending these catalogs of transformations is complex and time-consuming. A similar phenomenon appears in intelligent tutoring systems where instructors have to write cumbersome code transformations that describe "common faults" to fix similar student submissions to programming assignments. We present REFAZER, a technique for automatically generating program transformations. REFAZER builds on the observation that code edits performed by developers can be used as examples for learning transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of edits used by students to fix incorrect programming assignment submissions, we learn transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 59 scenarios of repetitive edits taken from 3 C# open-source projects, REFAZER learns the intended program transformation in 83% of the cases.

Connected through hyperedge HE-1 (induced by source paper "classcode an interactive teaching and learning environment for programming education in classrooms"):
  with members:
    - "tracediff debugging unexpected code behavior using trace divergences"
    - "mudslide a spatially anchored census of student confusion for online lecture videos"
    - "rimes embedding interactive multimedia exercises in lecture videos"

Connected through hyperedge HE-2 (induced by source paper "ifixr bug report driven program repair"):
  with members:
    - "d c a divide and conquer approach to ir based bug localization"
    - "tbar revisiting template based automated program repair"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "getafix learning to fix bugs automatically"
    - "avatar fixing semantic bugs with fix patterns of static analysis violations"
    - "on reliability of patch correctness assessment"
    - "fixminer mining relevant fix patterns for automated program repair"

Connected through hyperedge HE-3 (induced by source paper "tbar revisiting template based automated program repair"):
  with members:
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "avatar fixing semantic bugs with fix patterns of static analysis violations"
    - "on reliability of patch correctness assessment"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic software repair a bibliography"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-4 (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-5 (induced by source paper "deep learning for bug localization in student programs"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "deep reinforcement learning for programming language correction"
    - "tipsc tips and corrections for programming moocs"
    - "axiomatic attribution for deep networks"
    - "a survey of machine learning for big code and naturalness"
    - "automated clustering and program repair for introductory programming assignments"
    - "semi supervised verified feedback generation"
    - "sk_p a neural program corrector for moocs"

Connected through hyperedge HE-6 (induced by source paper "getafix learning to fix bugs automatically"):
  with members:
    - "learning to represent edits"
    - "deepbugs a learning approach to name based bug detection"
    - "coming a tool for mining change pattern instances from git commits"
    - "learning quick fixes from code repositories"
    - "semantic code repair using neuro symbolic transformation networks"
    - "a survey of machine learning for big code and naturalness"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "npefix automatic runtime repair of null pointer exceptions in java"

Connected through hyperedge HE-7 (induced by source paper "learning and suggesting source code changes from version history a systematic review"):
  with members:
    - "learning quick fixes from code repositories"
    - "connecting software metrics across versions to predict defects"
    - "refdiff detecting refactorings in version histories"

Connected through hyperedge HE-8 (induced by source paper "learning to generate corrective patches using neural machine translation"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "how different are different diff algorithms in git use histogram for code changes"
    - "automatic software repair a bibliography"
    - "tree2tree neural translation model for learning source code changes"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "neural machine translation and sequence to sequence models a tutorial"

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)