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: "do the fix ingredients already exist an empirical inquiry into the redundancy assumptions of program repair approaches"
  Abstract: Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of ``temporal redundancy''. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.

Connected through hyperedge HE-1 (induced by source paper "sorting and transforming program repair ingredients via deep learning code similarities"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "automatic software repair a bibliography"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "sk_p a neural program corrector for moocs"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "learning api usages from bytecode a statistical approach"

Connected through hyperedge HE-2 (induced by source paper "empirical review of java program repair tools a large scale experiment on 2 141 bugs and 23 551 repair attempts"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "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"
    - "automatic software repair a bibliography"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"

Connected through hyperedge HE-3 (induced by source paper "astor exploring the design space of generate and validate program repair beyond genprog"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "arja automated repair of java programs via multi objective genetic programming"
    - "an empirical analysis of the influence of fault space on search based automated program repair"
    - "identifying patch correctness in test based program repair"
    - "nopol automatic repair of conditional statement bugs in java programs"

Connected through hyperedge HE-4 (induced by source paper "an empirical study on learning bug fixing patches in the wild via neural machine translation"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "the adverse effects of code duplication in machine learning models of code"
    - "automatic software repair a bibliography"
    - "deep reinforcement learning for programming language correction"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"

Connected through hyperedge HE-5 (induced by source paper "codit code editing with tree based neuralmachine translation"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "learning to represent edits"
    - "building language models for text with named entities"
    - "tree to tree neural networks for program translation"
    - "opennmt open source toolkit for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"

Connected through hyperedge HE-6 (induced by source paper "automatic software repair a bibliography"):
  with members:
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "principles of antifragile software"
    - "automatic repair of infinite loops"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "automatic repair of real bugs an experience report on the defects4j dataset"
    - "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software repair"
    - "automatic repair of buggy if conditions and missing preconditions with smt"
    - "automated fixing of programs with contracts"

Connected through hyperedge HE-7 (induced by source paper "the remarkable role of similarity in redundancy based program repair"):
  with members:
    - "astor exploring the design space of generate and validate program repair beyond genprog"
    - "revisiting ssfix for better program repair"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "code2seq generating sequences from structured representations of code"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learning to represent programs with graphs"
    - "precise condition synthesis for program repair"

Connected through hyperedge HE-8 (induced by source paper "avatar fixing semantic bugs with fix patterns of static analysis violations"):
  with members:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"
    - "learning quick fixes from code repositories"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "impact of tool support in patch construction"
    - "mining fix patterns for findbugs violations"

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)