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 how to mutate source code from bug fixes"
  Abstract: Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While some recent papers have tried to devise domain-specific or general purpose mutator operators by manually analyzing real faults, such an activity is effort- (and error-) prone and does not deal with an important practical question as to how to really mutate a given source code element. We propose a novel approach to automatically learn mutants from faults in real programs. First, our approach processes bug fixing changes using fine-grained differencing, code abstraction, and change clustering. Then, it learns mutation models using a deep learning strategy. We have trained and evaluated our technique on a set of ~787k bugs mined from GitHub. Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model). Moreover, over 98% of the automatically generated mutants are lexically and syntactically correct.

Connected through hyperedge HE-1 (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"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "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-2 (induced by source paper "deepmutation a neural mutation tool"):
  with members:
    - "massive exploration of neural machine translation architectures"
    - "enabling mutation testing for android apps"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "analyzing the context of bug fixing changes in the openstack cloud computing platform"):
  with members:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "refdiff detecting refactorings in version histories"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "automatically extracting instances of code change patterns with ast analysis"

Connected through hyperedge HE-4 (induced by source paper "how bad can a bug get an empirical analysis of software failures in the openstack cloud computing platform"):
  with members:
    - "a survey of linguistic structures for application level fault tolerance"
    - "automatically extracting instances of code change patterns with ast analysis"

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)