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: "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
  Abstract: Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users' privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals' trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual's trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.

Connected through hyperedge HE-1 (induced by source paper "nowhere to hide cross modal identity leakage between biometrics and devices"):
  with members:
    - "autonomous learning for face recognition in the wild via ambient wireless cues"
    - "voxceleb2 deep speaker recognition"
    - "systematic classification of side channel attacks a case study for mobile devices"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a study of mac address randomization in mobile devices and when it fails"
    - "armageddon cache attacks on mobile devices"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "privacy in trajectory micro data publishing a survey"):
  with members:
    - "towards matching user mobility traces in large scale datasets"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "where you are is who you are user identification by matching statistics"
    - "spatio temporal techniques for user identification by means of gps mobility data"
    - "tracking human mobility using wifi signals"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "protecting locations with differential privacy under temporal correlations"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-3 (induced by source paper "please forget where i was last summer the privacy risks of public location meta data"):
  with members:
    - "the long road to computational location privacy a survey"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "a survey of location prediction on twitter"
    - "knock knock who s there membership inference on aggregate location data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "walk2friends inferring social links from mobility profiles"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"

Connected through hyperedge HE-4 (induced by source paper "protecting spatiotemporal event privacy in continuous location based services"):
  with members:
    - "the long road to computational location privacy a survey"
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "priste from location privacy to spatiotemporal event privacy"
    - "when and where do you want to hide recommendation of location privacy preferences with local differential privacy"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "quantifying differential privacy under temporal correlations"
    - "privacy games optimal user centric data obfuscation"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-5 (induced by source paper "under the hood of membership inference attacks on aggregate location time series"):
  with members:
    - "on the lack of location privacy in crowdsourcing applications"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "privacy friendly mobility analytics using aggregate location data"
    - "geo spotting mining online location based services for optimal retail store placement"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-6 (induced by source paper "a differentially private algorithm for range queries on trajectories"):
  with members:
    - "principled evaluation of differentially private algorithms using dpbench"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private grids for geospatial data"
    - "differentially private spatial decompositions"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-7 (induced by source paper "the tradeoff between the utility and risk of location data and implications for public good"):
  with members:
    - "mapping the privacy utility tradeoff in mobile phone data for development"
    - "estimating individual employment status using mobile phone network data"
    - "on the anonymizability of mobile traffic datasets"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-8 (induced by source paper "breadcrumbs a feature rich mobility dataset with point of interest annotation"):
  with members:
    - "discovering demographic data of users from the evolution of their spatio temporal entropy"
    - "geodabs trajectory indexing meets fingerprinting at scale"
    - "walk2friends inferring social links from mobility profiles"
    - "once upon a crime towards crime prediction from demographics and mobile data"

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)