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: "xml entity architecture for efficient software integration"
  Abstract: This paper proposed xml entities based architectural implementation to improve integration between multiple third party vendor software systems with incompatible xml schema. The xml entity architecture implementation showed that the lines of code change required for mapping the schema between in house software and three other vendor schema, decreased by 5.2%, indicating an improvement in quality. The schema mapping development time decreased by 3.8% and overall release time decreased by 5.3%, indicating an improvement in productivity. The proposed technique proved that using xml entities and XSLT transforms is more efficient in terms of coding effort and deployment complexity when compared to mapping the schema using object oriented scripting language such as C#.

Connected through hyperedge HE-1 (induced by source paper "multimodal affect analysis for product feedback assessment"):
  with members:
    - "multimodal affect recognition using kinect"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-2 (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"
    - "self contained cross cutting pipeline software architecture"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "multimodal affect recognition using kinect"
    - "emofit affect monitoring system for sedentary jobs"

Connected through hyperedge HE-3 (induced by source paper "dynamic probabilistic network based human action recognition"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "multimodal affect analysis for product feedback assessment"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "affect intensity estimation using multiple modalities"
    - "learning the structure of dynamic probabilistic networks"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-4 (induced by source paper "automated prediction of temporal relations"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"
    - "learning semantic script knowledge with event embeddings"
    - "learning sentence internal temporal relations"

Connected through hyperedge HE-5 (induced by source paper "embracing agile methodology during devops developer internship program"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "structured unit testable templated code for efficient code review process"
    - "emofit affect monitoring system for sedentary jobs"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-6 (induced by source paper "aggressive actions and anger detection from multiple modalities using kinect"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "structured unit testable templated code for efficient code review process"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-7 (induced by source paper "self contained cross cutting pipeline software architecture"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "affect intensity estimation using multiple modalities"

Connected through hyperedge HE-8 (induced by source paper "multimodal affect recognition using kinect"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "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)