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: "tydr track your daily routine android app for tracking smartphone sensor and usage data"
  Abstract: We present the Android app TYDR (Track Your Daily Routine) which tracks smartphone sensor and usage data and utilizes standardized psychometric personality questionnaires. With the app, we aim at collecting data for researching correlations between the tracked smartphone data and the user's personality in order to predict personality from smartphone data. In this paper, we highlight our approaches in addressing the challenges in developing such an app. We optimize the tracking of sensor data by assessing the trade-off of size of data and battery consumption and granularity of the stored information. Our user interface is designed to incentivize users to install the app and fill out questionnaires. TYDR processes and visualizes the tracked sensor and usage data as well as the results of the personality questionnaires. When developing an app that will be used in psychological studies, requirements posed by ethics commissions / institutional review boards and data protection officials have to be met. We detail our approaches concerning those requirements regarding the anonymized storing of user data, informing the users about the data collection, and enabling an opt-out option. We present our process for anonymized data storing while still being able to identify individual users who successfully completed a psychological study with the app.

Connected through hyperedge HE-1 (induced by source paper "affinity a system for latent user similarity comparison on texting data"):
  with members:
    - "context data categories and privacy model for mobile data collection apps"
    - "do you like what i like similarity estimation in proximity based mobile social networks"
    - "towards psychometrics based friend recommendations in social networking services"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "creating a live public short message service corpus the nus sms corpus"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-2 (induced by source paper "collaborating with users in proximity for decentralized mobile recommender systems"):
  with members:
    - "context data categories and privacy model for mobile data collection apps"
    - "do you like what i like similarity estimation in proximity based mobile social networks"
    - "towards psychometrics based friend recommendations in social networking services"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-3 (induced by source paper "do you like what i like similarity estimation in proximity based mobile social networks"):
  with members:
    - "context data categories and privacy model for mobile data collection apps"
    - "towards psychometrics based friend recommendations in social networking services"
    - "analysis of publicly disclosed information in facebook profiles"

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)