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: "hunting the ethereum smart contract color inspired inspection of potential attacks"
  Abstract: Blockchain and Cryptocurrencies are gaining unprecedented popularity and understanding. Meanwhile, Ethereum is gaining a significant popularity in the blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contract and decentralized applications (Dapps). This new paradigm of applications opens the door to many possibilities and opportunities. However, the security of Ethereum smart contracts has not received much attention; several Ethereum smart contracts malfunctioning have recently been reported. Unlike many previous works that have applied static and dynamic analyses to find bugs in smart contracts, we do not attempt to define and extract any features; instead we focus on reducing the expert's labor costs. We first present a new in-depth analysis of potential attacks methodology and then translate the bytecode of solidity into RGB color code. After that, we transform them to a fixed-sized encoded image. Finally, the encoded image is fed to convolutional neural network (CNN) for automatic feature extraction and learning, detecting compiler bugs of Ethereum smart contract.

Connected through hyperedge HE-1 (induced by source paper "an overview on smart contracts challenges advances and platforms"):
  with members:
    - "the art of the scam demystifying honeypots in ethereum smart contracts"
    - "blockchain for internet of things a survey"
    - "evmfuzz differential fuzz testing of ethereum virtual machine"
    - "tool demonstration fsolidm for designing secure ethereum smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "ethir a framework for high level analysis of ethereum bytecode"
    - "sabre protecting bitcoin against routing attacks"
    - "a survey on the security of blockchain systems"

Connected through hyperedge HE-2 (induced by source paper "sok blockchain technology and its potential use cases"):
  with members:
    - "decentralization in bitcoin and ethereum networks"
    - "a survey on security and privacy issues of bitcoin"
    - "a new look at the refund mechanism in the bitcoin payment protocol"
    - "consensus in the age of blockchains"
    - "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies"
    - "solida a blockchain protocol based on reconfigurable byzantine consensus"
    - "bitcoin ng a scalable blockchain protocol"
    - "a first look at the usability of bitcoin key management"

Connected through hyperedge HE-3 (induced by source paper "r2 d2 color inspired convolutional neural network cnn based android malware detections"):
  with members:
    - "a survey on the security of blockchain systems"
    - "tensorflow a system for large scale machine learning"
    - "character level convolutional networks for text classification"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

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)