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: "privacy in sensor driven human data collection a guide for practitioners"
  Abstract: In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks or collecting their data for self-tracking purposes (quantified-self movement). Across the sciences, researchers conduct studies collecting data with an unprecedented resolution and scale. Using computational power combined with mathematical models, such rich datasets can be mined to infer underlying patterns, thereby providing insights into human nature. Much of the data collected is sensitive. It is private in the sense that most individuals would feel uncomfortable sharing their collected personal data publicly. For this reason, the need for solutions to ensure the privacy of the individuals generating data has grown alongside the data collection efforts. Out of all the massive data collection efforts, this paper focuses on efforts directly instrumenting human behavior, and notes that -- in many cases -- the privacy of participants is not sufficiently addressed. For example, study purposes are often not explicit, informed consent is ill-defined, and security and sharing protocols are only partially disclosed. This paper provides a survey of the work related to addressing privacy issues in research studies that collect detailed sensor data on human behavior. Reflections on the key problems and recommendations for future work are included. We hope the overview of the privacy-related practices in massive data collection studies can be used as a frame of reference for practitioners in the field. Although focused on data collection in an academic context, we believe that many of the challenges and solutions we identify are also relevant and useful for other domains where massive data collection takes place, including businesses and governments.

Connected through hyperedge HE-1 (induced by source paper "offline behaviors of online friends"):
  with members:
    - "inferring person to person proximity using wifi signals"
    - "tracking human mobility using wifi signals"
    - "rhythms of social interaction messaging within a massive online network"

Connected through hyperedge HE-2 (induced by source paper "network structure inference a survey motivations methods and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network model selection using task focused minimum description length"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "network model selection for task focused attributed network inference"
    - "evaluating social networks using task focused network inference"
    - "on the efficiency of the information networks in social media"
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-3 (induced by source paper "inferring person to person proximity using wifi signals"):
  with members:
    - "tracking human mobility using wifi signals"
    - "measuring large scale social networks with high resolution"
    - "brief encounters sensing modeling and visualizing urban mobility and copresence networks"

Connected through hyperedge HE-4 (induced by source paper "privacy leakage in mobile computing tools methods and characteristics"):
  with members:
    - "i know what leaked in your pocket uncovering privacy leaks on android apps with static taint analysis"
    - "a case of collusion a study of the interface between ad libraries and their apps"
    - "longitudinal analysis of android ad library permissions"
    - "participatory privacy enabling privacy in participatory sensing"
    - "adsplit separating smartphone advertising from applications"

Connected through hyperedge HE-5 (induced by source paper "human data interaction the human face of the data driven society"):
  with members:
    - "sensing as a service and big data"
    - "a market for unbiased private data paying individuals according to their privacy attitudes"

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)