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: "easy over hard a case study on deep learning"
  Abstract: While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work.     For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method.     We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.

Connected through hyperedge HE-1 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models"):
  with members:
    - "activity based analysis of open source software contributors roles and dynamics"
    - "analysis and detection of information types of open source software issue discussions"
    - "how do the open source communities address usability and ux issues an exploratory study"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-2 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "feature maps a comprehensible software representation for design pattern detection"
    - "on learning meaningful code changes via neural machine translation"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "when deep learning met code search"
    - "a comprehensive study on deep learning bug characteristics"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "on the feasibility of transfer learning code smells using deep learning"):
  with members:
    - "a machine learning based ensemble method for anti patterns detection"
    - "lstm a search space odyssey"
    - "a survey of machine learning for big code and naturalness"
    - "a syntactic neural model for general purpose code generation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "latent predictor networks for code generation"
    - "sk_p a neural program corrector for moocs"
    - "a convolutional attention network for extreme summarization of source code"

Connected through hyperedge HE-4 (induced by source paper "software engineering practice in the development of deep learning applications"):
  with members:
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "machine learning testing survey landscapes and horizons"
    - "requirements engineering challenges in building ai based complex systems"
    - "a comprehensive study on deep learning bug characteristics"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "dlfuzz differential fuzzing testing of deep learning systems"

Connected through hyperedge HE-5 (induced by source paper "mat a simple yet strong baseline for identifying self admitted technical debt"):
  with members:
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "simple baselines for human pose estimation and tracking"
    - "the effectiveness of instance normalization a strong baseline for single image dehazing"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "bellwethers a baseline method for transfer learning"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "semantic source code models using identifier embeddings"):
  with members:
    - "code2vec learning distributed representations of code"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "learning word vectors for 157 languages"
    - "advances in pre training distributed word representations"
    - "toward incorporation of relevant documents in word2vec"
    - "a survey of machine learning for big code and naturalness"
    - "a syntactic neural model for general purpose code generation"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "how to dodge complex software analytics"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"

Connected through hyperedge HE-8 (induced by source paper "enhanced labeling of issue reports with f 3 t"):
  with members:
    - "how to dodge complex software analytics"
    - "replication can improve prior results a github study of pull request acceptance"
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "we don t need another hero the impact of heroes on software development"
    - "bellwethers a baseline method for transfer learning"
    - "why is differential evolution better than grid search for tuning defect predictors"

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)