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 "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  Abstract: Automated generate-and-validate (G&V) program repair techniques typically rely on hard-coded rules, only fix bugs following specific patterns, and are hard to adapt to different programming languages. We propose ENCORE, a new G&V technique, which uses ensemble learning on convolutional neural machine translation (NMT) models to automatically fix bugs in multiple programming languages. #R##N#We take advantage of the randomness in hyper-parameter tuning to build multiple models that fix different bugs and combine them using ensemble learning. This new convolutional NMT approach outperforms the standard long short-term memory (LSTM) approach used in previous work, as it better captures both local and long-distance connections between tokens. #R##N#Our evaluation on two popular benchmarks, Defects4J and QuixBugs, shows that ENCORE fixed 42 bugs, including 16 that have not been fixed by existing techniques. In addition, ENCORE is the first G&V repair technique to be applied to four popular programming languages (Java, C++, Python, and JavaScript), fixing a total of 67 bugs across five benchmarks.

Includes members:
  Member "a comprehensive study of automatic program repair on the quixbugs benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automated patch assessment for program repair at scale")
  Member "sequencer sequence to sequence learning for end to end program repair":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness for code")
  Member "approaching neural grammatical error correction as a low resource machine translation task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "reaching human level performance in automatic grammatical error correction an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "a multilayer convolutional encoder decoder neural network for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "fixminer mining relevant fix patterns for automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ifixr bug report driven program repair")
  Member "tree2tree neural translation model for learning source code changes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching a database of source codes using contextualized code search")

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)