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: "location privacy in cognitive radio networks a survey"
  Abstract: Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great benefits that CRNs offer in terms of their ability to improve spectrum utilization efficiency, they suffer from user location privacy issues. Knowing that their whereabouts may be exposed can discourage users from joining and participating in the CRNs, thereby potentially hindering the adoption and deployment of this technology in future generation networks. The location information leakage issue in the CRN context has recently started to gain attention from the research community due to its importance, and several research efforts have been made to tackle it. However, to the best of our knowledge, none of these works have tried to identify the vulnerabilities that are behind this issue or discuss the approaches that could be deployed to prevent it. In this paper, we try to fill this gap by providing a comprehensive survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions that have been proposed in the literature to cope with this location privacy issue. We also discuss some open research problems, related to this issue, that need to be overcome by the research community to take advantage of the benefits of this key CRN technology without having to sacrifice the users’ privacy.

Connected through hyperedge HE-1 (induced by source paper "the long road to computational location privacy a survey"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "adaptive location privacy with alp"
    - "constructing elastic distinguishability metrics for location privacy"
    - "protecting locations with differential privacy under temporal correlations"
    - "time distortion anonymization for the publication of mobility data with high utility"

Connected through hyperedge HE-2 (induced by source paper "location privacy in cognitive radios with multi server private information retrieval"):
  with members:
    - "preserving the location privacy of secondary users in cooperative spectrum sensing"
    - "when the hammer meets the nail multi server pir for database driven crn with location privacy assurance"
    - "location privacy preservation in database driven wireless cognitive networks through encrypted probabilistic data structures"
    - "an efficient technique for protecting location privacy of cooperative spectrum sensing users"
    - "lpos location privacy for optimal sensing in cognitive radio networks"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-3 (induced by source paper "when the hammer meets the nail multi server pir for database driven crn with location privacy assurance"):
  with members:
    - "preserving the location privacy of secondary users in cooperative spectrum sensing"
    - "location privacy preservation in database driven wireless cognitive networks through encrypted probabilistic data structures"
    - "an efficient technique for protecting location privacy of cooperative spectrum sensing users"
    - "lpos location privacy for optimal sensing in cognitive radio networks"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-4 (induced by source paper "location privacy preservation in database driven wireless cognitive networks through encrypted probabilistic data structures"):
  with members:
    - "preserving the location privacy of secondary users in cooperative spectrum sensing"
    - "an efficient technique for protecting location privacy of cooperative spectrum sensing users"
    - "lpos location privacy for optimal sensing in cognitive radio networks"
    - "geo indistinguishability differential privacy for location based systems"

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.NI(Networking and Internet Architecture)