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: "computational intelligence for qualitative coaching diagnostics automated assessment of tennis swings to improve performance and safety"
  Abstract: Coaching technology, wearables and exergames can provide quantitative feedback based on measured activity, but there is little evidence of qualitative feedback to aid technique improvement. To achieve personalised qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique. Three-dimensional tennis motion data were acquired from multi-camera video (22 backhands and 21 forehands, including common errors). Selected coaching rules were transferred to adaptive assessment modules able to learn from data, evolve their internal structures and produce autonomous personalised feedback. The generated qualitative assessment for each swing sample was compared with the expert. The prototype demonstrated autonomous assessment on future data based on learning from prior examples, aligned with skill level, coaching scenarios and coaching rules. The generated intuitive diagnostic feedback consisted of elements of safety and performance for tennis swing technique. For safety aspects of the relative swing width, the prototype showed improved assessment (from 81% to 91%) when taking into account occluded parts of the pelvis. The next-generation augmented coaching, personalised rehabilitation and exergaming systems will learn from small data, adapt and provide personalised intuitive autonomous assessment of motion data aligned with specified coaching programme and feedback requirements.

Connected through hyperedge HE-1 (induced by source paper "big data architecture in czech republic healthcare service requirements tpc h benchmarks and vertica"):
  with members:
    - "towards the next generation of exergames flexible and personalised assessment based identification of tennis swings"
    - "a hybrid ict solution for smart meter data analytics"
    - "the vertica analytic database c store 7 years later"

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)