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: "riot a novel stochastic method for rapidly configuring cloud based workflows"
  Abstract: Traditional tools for configuring cloud services can run much slower than the workflows they are trying to optimize. For example, in the case studies reported here, we find cases where (using traditional methods) it takes hours to find ways to make a workflow terminate in tens of seconds. Such slow optimizers are a poor choice of tools for reacting to changing operational environmental conditions. Hence, they are unsuited for cloud services that support rapidly changing workflows, e.g., scientific workflows or workflows from the media or telecommunication industries. #R##N#To solve this problem, this paper presents RIOT (Randomized Instance Order Types), a new configuration tool. RIOT has a very low optimization overhead-- often, less than 10\% of the system runtime, especially for every complex workflow. Instead of simulating many configurations, RIOT uses a novel surrogate sampling method to quickly find promising solutions. As shown by this paper, RIOT achieves comparable results to the other approaches but does so in a fraction of the time.

Connected through hyperedge HE-1 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "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"
    - "finding better active learners for faster literature reviews"
    - "transfer learning for improving model predictions in highly configurable software"
    - "short er reasoning about larger requirements models"
    - "using bad learners to find good configurations"

Connected through hyperedge HE-2 (induced by source paper "is one hyperparameter optimizer enough"):
  with members:
    - "applications of psychological science for actionable analytics"
    - "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"
    - "can you explain that better comprehensible text analytics for se applications"
    - "building better quality predictors using epsilon dominance"
    - "bellwethers a baseline method for transfer learning"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-3 (induced by source paper "sampling as a baseline optimizer for search based software engineering"):
  with members:
    - "beyond evolutionary algorithms for search based software engineering"
    - "easy over hard a case study on deep learning"
    - "a note on the triangle inequality for the jaccard distance"
    - "tuning for software analytics"

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)