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: "self contained cross cutting pipeline software architecture"
  Abstract: Layered software architecture contains several intra-layer and inter-layer dependencies. Each layer depends on shared components making it difficult to release a code change, bug fix or feature without exhaustive testing and having to build the entire software code base. This paper proposed self-contained, cross-cutting pipeline architecture (SCPA) that is independent of existing layers. We chose 2 open source projects and 3 internal intern projects that used n-tier architecture and applied the SCPA to release subsequent feature additions and any bug fixes. The SCPA decreased the release time by 42.99%. The lines of delivered code (LOC), increased by 22.58%. The number of defects found in existing functionality decreased by 85.54%. The SCPA also provided ability to roll back or switch off the new feature quickly. SCPA proved a suitable architecture for agile software development and continuous deployment.

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"
    - "structured unit testable templated code for efficient code review process"
    - "dynamic probabilistic network based human action recognition"
    - "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 "xml entity architecture for efficient software integration"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-3 (induced by source paper "structured unit testable templated code for efficient code review process"):
  with members:
    - "affect intensity estimation using multiple modalities"

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)