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: "privacyproxy leveraging crowdsourcing and in situ traffic analysis to detect and mitigate information leakage"
  Abstract: Smartphone apps often transmit personally identifiable information (PII) without the user's knowledge. To address this issue, we present PrivacyProxy, a system that monitors outbound network traffic and generates app-specific signatures to represent privacy sensitive data being shared, all without any modifications to the OS. We use a crowdsourcing based approach to detect likely PII in an adaptive and scalable manner by anonymously combining signatures from different users of the same app. Our system design is itself privacy sensitive as we do not see users' network traffic and instead rely on cryptographically hashed signatures. We present the design and implementation of PrivacyProxy and evaluate its effectiveness in detecting various PIIs through a lab study and a field deployment. Our field study shows that even without any user labeling of PII, PrivacyProxy automatically detects various forms of PII with a precision of 0.81. We also show that the performance overhead of PrivacyProxy is under 13% and majority of the users report no perceptible impact on battery life or the network.

Connected through hyperedge HE-1 (induced by source paper "a federated learning approach for mobile packet classification"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated heavy hitters discovery with differential privacy"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-2 (induced by source paper "third party tracking in the mobile ecosystem"):
  with members:
    - "measuring third party tracker power across web and mobile"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"
    - "exposing the hidden web an analysis of third party http requests on 1 million websites"
    - "an empirical study of mobile ad targeting"

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.CR(Cryptography and Security)