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: "emofit affect monitoring system for sedentary jobs"
  Abstract: Emotional and physical well-being at workplace is important for a positive work environment and higher productivity. Jobs such as software programming lead to a sedentary lifestyle and require high interaction with computers. Working at the same job for years can cause a feeling of intellectual stagnation and lack of drive. Many employees experience lack of motivation, mild to extreme depression due to reasons such as aversion towards job responsibilities and incompatibility with coworkers or boss. This research proposed an affect monitoring system EmoFit that would play the role of psychological and physical health trainer. The day to day computer activity and body language was analyzed to detect the physical and emotional well-being of the user. Keystrokes, activity interruptions, eye tracking, facial expressions, body posture and speech were monitored to gauge the users health. The system also provided activities such as at-desk exercise and stress relief game and motivational quotes in an attempt to promote users well-being. The experimental results and positive feedback from test subjects showed that EmoFit would help improve emotional and physical well-being at jobs that involve significant computer usage.

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"
    - "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"

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"
    - "structured unit testable templated code for efficient code review process"
    - "xml entity architecture for efficient software integration"
    - "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.HC(Human-Computer Interaction)