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: "sd eqr a new technique to use qr codestm in cryptography"
  Abstract: In this paper the author present a new technique of using QR Codes (commonly known as 'Quick Respond Codes') in the field of Cryptography. QR Codes are mainly used to convey or store messages because they have higher or large storage capacity than any other normal conventional 'barcodes'. In this paper the primary focus will be on storing messages in encrypted format with a password and send it to the required destination hiding in a QR Code, without being tracked or decrypted properly by any hacker or spyware. Since QR Codes have fast response time and have large storage capacity, QR Codes can be used perfectly to send encrypted data (messages) to the receiver. This method will be suitable in any business house, government sectors, communication network to send their encrypted messages faster to the destination. Or a person can even use this method to keep his important documents, like passport number, pan-card id, social security number, perfectly secured with him all the time, without the information getting leaked to outside world. The new method is achieved by entering the message along with a password. This password will generate a secret code, which will be added to each digit or alphabet in the numbers or text entered in the message (which is needed to be encrypted) and generate the first phase of encryption. That newly generated encrypted message will again be encrypted using various other methods to generate the final encrypted message.

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:
    - "confidential encrypted data hiding and retrieval using qr authentication system"
    - "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"

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:
    - "confidential encrypted data hiding and retrieval using qr authentication system"
    - "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 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)