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: "powerspy location tracking using mobile device power analysis"
  Abstract: Modern mobile platforms like Android enable applications to read aggregate power usage on the phone. This information is considered harmless and reading it requires no user permission or notification. We show that by simply reading the phone's aggregate power consumption over a period of a few minutes an application can learn information about the user's location. Aggregate phone power consumption data is extremely noisy due to the multitude of components and applications that simultaneously consume power. Nevertheless, by using machine learning algorithms we are able to successfully infer the phone's location. We discuss several ways in which this privacy leak can be remedied.

Connected through hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-2 (induced by source paper "maya falsifying power sidechannels with dynamic control"):
  with members:
    - "dvfs as a security failure of trustzone enabled heterogeneous soc"
    - "high efficiency power side channel attack immunity using noise injection in attenuated signature domain"
    - "power side channels in security ics hardware countermeasures"
    - "a study on power side channels on mobile devices"
    - "thermal covert channels on multi core platforms"
    - "design method for constant power consumption of differential logic circuits"

Connected through hyperedge HE-3 (induced by source paper "analyzing defense strategies against mobile information leakages a game theoretic approach"):
  with members:
    - "side channel inference attacks on mobile keypads using smartwatches"
    - "towards inferring mechanical lock combinations using wrist wearables as a side channel"

Connected through hyperedge HE-4 (induced by source paper "attriguard a practical defense against attribute inference attacks via adversarial machine learning"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "privacy games optimal user centric data obfuscation"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "adversarial wifi sensing using a single smartphone"):
  with members:
    - "position tracking for virtual reality using commodity wifi"
    - "a study of mac address randomization in mobile devices and when it fails"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "protecting locations with differential privacy under temporal correlations"
    - "optimal geo indistinguishable mechanisms for location privacy"

Connected through hyperedge HE-6 (induced by source paper "light ears information leakage via smart lights"):
  with members:
    - "irexf data exfiltration from air gapped networks by infrared remote control signals"
    - "side channel inference attacks on mobile keypads using smartwatches"
    - "air jumper covert air gap exfiltration infiltration via security cameras infrared ir"
    - "towards inferring mechanical lock combinations using wrist wearables as a side channel"
    - "don t skype type acoustic eavesdropping in voice over ip"

Connected through hyperedge HE-7 (induced by source paper "analysis of location data leakage in the internet traffic of android based mobile devices"):
  with members:
    - "poisoning behavioral malware clustering"
    - "understanding tor usage with privacy preserving measurement"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-8 (induced by source paper "pinme tracking a smartphone user around the world"):
  with members:
    - "we can track you if you take the metro tracking metro riders using accelerometers on smartphones"

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)