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: "demo recon revealing and controlling pii leaks in mobile network traffic"
  Abstract: It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII) [1] and users are generally unaware and unable to stop them [2]; however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties.   This demo presents the design, implementation, and evaluation of ReCon: a cross-platform (iOS, Android, Windows) system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon, which has been accepted as a paper to the Mobisys conference, leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. For this demo, we will show how ReCon detects PII in real time, allows users to provide feedback on detected PII, and provides users with controls for blocking or modifying PII leaks. The system is publicly available and we will also help any interested users install it on their mobile devices.

Connected through hyperedge HE-1 (induced by source paper "security and privacy approaches in mixed reality a literature survey"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "magnet a two pronged defense against adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "cardea context aware visual privacy protection from pervasive cameras"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "risk perceptions for wearable devices"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-2 (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-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"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "walk2friends inferring social links from mobility profiles"
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"

Connected through hyperedge HE-4 (induced by source paper "devicewatch identifying compromised mobile devices through network traffic analysis and graph inference"):
  with members:
    - "characterizing location based mobile tracking in mobile ad networks"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "node2vec scalable feature learning for networks"
    - "personalized security indicators to detect application phishing attacks in mobile platforms"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-5 (induced by source paper "who s tracking sensitive domains"):
  with members:
    - "tracking the pixels detecting web trackers via analyzing invisible pixels"
    - "cookie synchronization everything you always wanted to know but were afraid to ask"
    - "tracing information flows between ad exchanges using retargeted ads"
    - "variations in tracking in relation to geographic location"

Connected through hyperedge HE-6 (induced by source paper "the tv is smart and full of trackers towards understanding the smart tv advertising and tracking ecosystem"):
  with members:
    - "antmonitor system and applications"
    - "open connect everywhere a glimpse at the internet ecosystem through the lens of the netflix cdn"
    - "antmonitor a system for on device mobile network monitoring and its applications"

Connected through hyperedge HE-7 (induced by source paper "characterizing location based mobile tracking in mobile ad networks"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "variations in tracking in relation to geographic location"

Connected through hyperedge HE-8 (induced by source paper "beyond google play a large scale comparative study of chinese android app markets"):
  with members:
    - "dating with scambots understanding the ecosystem of fraudulent dating applications"
    - "androzoo collecting millions of android apps and their metadata for the research community"
    - "frauddroid automated ad fraud detection for android apps"
    - "an investigation into the use of common libraries in android apps"
    - "andlantis large scale android dynamic analysis"

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)