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: "context data categories and privacy model for mobile data collection apps"
  Abstract: Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smart-phone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user’s personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications.

Connected through hyperedge HE-1 (induced by source paper "affinity a system for latent user similarity comparison on texting data"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "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 "on gossip based information dissemination in pervasive recommender systems"):
  with members:
    - "affinity a system for latent user similarity comparison on texting data"
    - "collaborating with users in proximity for decentralized mobile recommender systems"
    - "federated meta learning for recommendation"
    - "do you like what i like similarity estimation in proximity based mobile social networks"
    - "federated meta learning with fast convergence and efficient communication"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-3 (induced by source paper "collaborating with users in proximity for decentralized mobile recommender systems"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "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-4 (induced by source paper "do you like what i like similarity estimation in proximity based mobile social networks"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "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)