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: "deep spying spying using smartwatch and deep learning"
  Abstract: Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. #R##N#In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. #R##N#The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. #R##N#The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.

Connected through hyperedge HE-1 (induced by source paper "undermining user privacy on mobile devices using ai"):
  with members:
    - "deepcloak adversarial crafting as a defensive measure to cloak processes"
    - "theory and practice of finding eviction sets"
    - "loophole timing attacks on shared event loops in chrome"
    - "perfweb how to violate web privacy with hardware performance events"
    - "autolock why cache attacks on arm are harder than you think"
    - "memory dos attacks in multi tenant clouds severity and mitigation"
    - "flush flush a fast and stealthy cache attack"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "deepcloak adversarial crafting as a defensive measure to cloak processes"):
  with members:
    - "cacheshield protecting legacy processes against cache attacks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "detecting cancer metastases on gigapixel pathology images"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "adversarial machine learning at scale"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-3 (induced by source paper "handwritten signature verification using hand worn devices"):
  with members:
    - "offline handwritten signature verification literature review"
    - "authenticating users through their arm movement patterns"

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)