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: "confidential encrypted data hiding and retrieval using qr authentication system"
  Abstract: Now, security and authenticity of data is a big challenge. To solve this problem, we propose an innovative method to authenticate the digital documents. In this paper, we propose a new method, where the marks obtained by a candidate will also be encoded in QR CodeTM in encrypted form, so that if an intruder tries to change the marks in the mark sheet then he can not do that in the QR CodeTM, because the encryption key is unknown to him. In this method, we encrypt the mark sheet data using the TTJSA encryption algorithm. The encrypted marks are entered inside QR code and that QR code is also printed with the original data of the mark sheet. The marks can then be retrieved from the QR code and can be decrypted using TTJSA decryption algorithm and then it can be verified with marks already there in the mark sheet.

Connected through hyperedge HE-1 (induced by source paper "mat cnn sopc motionless analysis of traffic using convolutional neural networks on system on a programmable chip"):
  with members:
    - "detection of human rights violations in images can convolutional neural networks help"
    - "a paradigm shift detecting human rights violations through web images"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "integral images efficient algorithms for their computation and storage in resource constrained embedded vision systems"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "securing majority attack in blockchain using machine learning and algorithmic game theory a proof of work"):
  with members:
    - "mat cnn sopc motionless analysis of traffic using convolutional neural networks on system on a programmable chip"
    - "analysis of hashrate based double spending"
    - "on the longest chain rule and programmed self destruction of crypto currencies"
    - "majority is not enough bitcoin mining is vulnerable"
    - "sd eqr a new technique to use qr codestm in cryptography"
    - "sd ree a cryptographic method to exclude repetition from a message"

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)