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: "using smartphone sensor technology for mental health research clear obstacles and hidden challenges"
  Abstract: Background: Smartphone sensor technology has great potential in providing behavioural markers of mental health. However, this promise has not yet been brought to fruition. Objective: To examine the challenges involved in developing an app to extract behavioural markers of mental health from passive sensor data. Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results from a feasibility study. Socialise, a smartphone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, GPS and battery status) and investigate the views and experiences of a group of people with a lived experience of mental health challenges (n=32). Results: On average, sensor data was obtained for 55% (Android) and 45% (iOS) of the scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes, a reduction of 2.5 hours or 12%. Despite this limited reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organisation that collects the data, and perceived impact on privacy were identified as the main factors for acceptability. Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting, and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of their use to be rigorously assessed.

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.CY(Computers and Society)