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: "exploring ways to mitigate sensor based smartphone fingerprinting"
  Abstract: Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result from manufacturing imperfections. Such measurements can be conducted surreptitiously in the browser and can be used to track users across applications, websites, and visits. #R##N#We analyze techniques to mitigate such device fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. To do this, we first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of (inaudible) audio stimulation to improve detection. We then collect measurements from a large collection of smartphones and evaluate the impact of calibration and obfuscation techniques on the classifier accuracy.

Connected through hyperedge HE-1 (induced by source paper "stealing pins via mobile sensors actual risk versus user perception"):
  with members:
    - "touchsignatures identification of user touch actions and pins based on mobile sensor data via javascript"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-2 (induced by source paper "smartphone fingerprinting via motion sensors analyzing feasiblity at large scale and studing real usage patterns"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-3 (induced by source paper "learning human identity from motion patterns"):
  with members:
    - "hmog a new biometric modality for continuous authentication of smartphone users"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "moddrop adaptive multi modal gesture recognition"
    - "using unlabeled data in a sparse coding framework for human activity recognition"

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)