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: "structured unit testable templated code for efficient code review process"
  Abstract: Modern software development teams are distributed across onsite and off-shore locations. Each team has developers with varying experience levels and English communication skills. In such a diverse development environment it is important to maintain the software quality, coding standards, timely delivery of features and bug fixes. It is also important to reduce testing effort, minimize side effects such as change in functionality, user experience or application performance. Code reviews are intended to control code quality. Unfortunately, many projects lack enforcement of processes and standards because of approaching deadlines, live production issues and lack of resource availability. This study examines a novel structured, unit testable templated code method to enforce code review standards with an intent to reduce coding effort, minimize revisions and eliminate functional and performance side effects on the system. The proposed method would also result in unit-testable code that can also be easily rolled back and increase team productivity. The baseline for traditional code review processes using metrics such as code review duration, bug regression rate, revision count was measured. These metrics were then compared with results from the proposed code review process that used structured unit testable templated code. The performance on 2 large enterprise level applications spanning over 2 years and 9 feature and maintenance release cycles was evaluated. The structured unit testable templated code method resulted in a decrease in total code review time, revision count and coding effort. It also decreased the number of live production issues caused by code churn or side effects of bug fix when compared to traditional code review process.

Connected through hyperedge HE-1 (induced by source paper "edge based grid super imposition for crowd emotion recognition"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "code definition analysis for call graph generation"
    - "dynamic probabilistic network based human action recognition"
    - "self contained cross cutting pipeline software architecture"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "multimodal affect recognition using kinect"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"

Connected through hyperedge HE-2 (induced by source paper "embracing agile methodology during devops developer internship program"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "xml entity architecture for efficient software integration"
    - "emofit affect monitoring system for sedentary jobs"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-3 (induced by source paper "aggressive actions and anger detection from multiple modalities using kinect"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-4 (induced by source paper "emofit affect monitoring system for sedentary jobs"):
  with members:
    - "xml entity architecture for efficient software integration"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-5 (induced by source paper "affect intensity estimation using multiple modalities"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "xml entity architecture for efficient software integration"

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)