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: "synesthesia detecting screen content via remote acoustic side channels"
  Abstract: We show that subtle acoustic noises emanating from within computer screens can be used to detect the content displayed on the screens. This sound can be picked up by ordinary microphones built into webcams or screens, and is inadvertently transmitted to other parties, e.g., during a videoconference call or archived recordings. It can also be recorded by a smartphone or "smart speaker" placed on a desk next to the screen, or from as far as 10 meters away using a parabolic microphone. #R##N#Empirically demonstrating various attack scenarios, we show how this channel can be used for real-time detection of on-screen text, or users' input into on-screen virtual keyboards. We also demonstrate how an attacker can analyze the audio received during video call (e.g., on Google Hangout) to infer whether the other side is browsing the web in lieu of watching the video call, and which web site is displayed on their screen.

Connected through hyperedge HE-1 (induced by source paper "oops had the silly thing in reverse optical injection attacks in through led status indicators"):
  with members:
    - "optical tempest"
    - "powerhammer exfiltrating data from air gapped computers through power lines"
    - "magneto covert channel between air gapped systems and nearby smartphones via cpu generated magnetic fields"
    - "beatcoin leaking private keys from air gapped cryptocurrency wallets"
    - "oops i think i scanned a malware"
    - "air jumper covert air gap exfiltration infiltration via security cameras infrared ir"
    - "xled covert data exfiltration from air gapped networks via router leds"
    - "usbee air gap covert channel via electromagnetic emission from usb"

Connected through hyperedge HE-2 (induced by source paper "electro magnetic side channel attack through learned denoising and classification"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "noise2noise learning image restoration without clean data"
    - "exploring the limits of weakly supervised pretraining"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mask r cnn"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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)