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: "natural language or not nlon a package for software engineering text analysis pipeline"
  Abstract: The use of natural language processing (NLP) is gaining popularity in software engineering. In order to correctly perform NLP, we must pre-process the textual information to separate natural language from other information, such as log messages, that are often part of the communication in software engineering. We present a simple approach for classifying whether some textual input is natural language or not. Although our NLoN package relies on only 11 language features and character tri-grams, we are able to achieve an area under the ROC curve performances between 0.976-0.987 on three different data sources, with Lasso regression from Glmnet as our learner and two human raters for providing ground truth. Cross-source prediction performance is lower and has more fluctuation with top ROC performances from 0.913 to 0.980. Compared with prior work, our approach offers similar performance but is considerably more lightweight, making it easier to apply in software engineering text mining pipelines. Our source code and data are provided as an R-package for further improvements.

Connected through hyperedge HE-1 (induced by source paper "a large scale in depth analysis of developers personalities in the apache ecosystem"):
  with members:
    - "establishing personal trust based connections in distributed teams"
    - "on developers personality in large scale distributed projects the case of the apache ecosystem"
    - "sentiment polarity detection for software development"
    - "a gold standard for emotion annotation in stack overflow"
    - "emotxt a toolkit for emotion recognition from text"
    - "a preliminary analysis on the effects of propensity to trust in distributed software development"
    - "25 tweets to know you a new model to predict personality with social media"
    - "links between the personalities styles and performance in computer programming"

Connected through hyperedge HE-2 (induced by source paper "on using machine learning to identify knowledge in api reference documentation"):
  with members:
    - "sentiment polarity detection for software development"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "easy over hard a case study on deep learning"
    - "an overview of gradient descent optimization algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"
    - "what should developers be aware of an empirical study on the directives of api documentation"

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)