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: "pinme tracking a smartphone user around the world"
  Abstract: With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.

Connected through hyperedge HE-1 (induced by source paper "darts deceiving autonomous cars with toxic signs"):
  with members:
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "exploring the space of black box attacks on deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial attacks on neural network policies"
    - "magnet a two pronged defense against adversarial examples"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "mitigating location privacy attacks on mobile devices using dynamic app sandboxing"):
  with members:
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "mining users significant driving routes with low power sensors"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-3 (induced by source paper "security of gps ins based on road location tracking systems"):
  with members:
    - "cascading denial of service attacks on wi fi networks"
    - "on the performance of adaptive packetized wireless communication links under jamming"
    - "on the capacity of rate adaptive packetized wireless communication links under jamming"

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)