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: "sequencer sequence to sequence learning for end to end program repair"
  Abstract: This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "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"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "neural network based graph embedding for cross platform binary code similarity detection"

Connected through hyperedge HE-3 (induced by source paper "on learning meaningful assert statements for unit test cases"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "a neural model for generating natural language summaries of program subroutines"
    - "learning how to mutate source code from bug fixes"
    - "learning to generate corrective patches using neural machine translation"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "six challenges for neural machine translation"
    - "deep api learning"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-5 (induced by source paper "learning to fix build errors with graph2diff neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "neural program repair by jointly learning to localize and repair"
    - "getafix learning to fix bugs automatically"
    - "a comprehensive survey on graph neural networks"
    - "generative code modeling with graphs"
    - "graph to sequence learning using gated graph neural networks"
    - "neural networks for modeling source code edits"

Connected through hyperedge HE-6 (induced by source paper "learning the relation between code features and code transforms with structured prediction"):
  with members:
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "towards an automated approach for bug fix pattern detection"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "automatic software repair a bibliography"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "automated patch assessment for program repair at scale"):
  with members:
    - "how different is it between machine generated and developer provided patches an empirical study on the correct patches generated by automated program repair techniques"
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "validation of automatically generated patches an appetizer"
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "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"
    - "empirical review of java program repair tools a large scale experiment on 2 141 bugs and 23 551 repair attempts"
    - "on reliability of patch correctness assessment"

Connected through hyperedge HE-8 (induced by source paper "how often do single statement bugs occur the manysstubs4j dataset"):
  with members:
    - "does unit tested code crash a case study of eclipse"
    - "the codrep machine learning on source code competition"
    - "automatic software repair a bibliography"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "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)