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: "towards safer smart contracts a sequence learning approach to detecting vulnerabilities"
  Abstract: Symbolic analysis of security exploits in smart contracts has demonstrated to be valuable for analyzing predefined vulnerability properties. While some symbolic tools perform complex analysis steps (which require predetermined invocation depth to search the execution paths), they employ fixed definitions of these vulnerabilities. However, vulnerabilities evolve. The number of contracts on blockchains like Ethereum has increased 176 fold since December 2015. If these symbolic tools fail to update over time, they could allow entire classes of vulnerabilities to go undetected, leading to unintended consequences. In this paper, we aim to have smart contracts that are less vulnerable to a broad class of emerging threats. In particular, we propose a novel approach of sequential learning of smart contract vulnerabilities using machine learning --- long-short term memory (LSTM) --- that perpetually learns from an increasing number of contracts handled over time, leading to safer smart contracts. Our experimental studies on approximately one million smart contracts for learning revealed encouraging results. A detection accuracy of 97% on contract vulnerabilities has been observed. In addition, our machine learning approach also correctly detected 76% of contract vulnerabilities that would otherwise be deemed as false positive errors by a symbolic tool. Last but not least, the proposed approach correctly identified a broader class of vulnerabilities when considering a subset of 10,000 contracts that are sampled from unflagged contracts.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (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"
    - "hunting the ethereum smart contract color inspired inspection of potential attacks"

Connected through hyperedge HE-3 (induced by source paper "ae gis shielding vulnerable smart contracts against attacks"):
  with members:
    - "slither a static analysis framework for smart contracts"
    - "soliditycheck quickly detecting smart contract problems through regular expressions"
    - "harvey a greybox fuzzer for smart contracts"
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "towards safer smart contracts a sequence learning approach to detecting security threats"

Connected through hyperedge HE-4 (induced by source paper "a survey on ethereum systems security vulnerabilities attacks and defenses"):
  with members:
    - "security and privacy on blockchain"
    - "exploring the attack surface of blockchain a systematic overview"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "metrics towards measuring cyber agility"
    - "selfish mining in ethereum"
    - "a survey of distributed consensus protocols for blockchain networks"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "ethir a framework for high level analysis of ethereum bytecode"

Connected through hyperedge HE-5 (induced by source paper "the art of the scam demystifying honeypots in ethereum smart contracts"):
  with members:
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "data mining for detecting bitcoin ponzi schemes"
    - "towards safer smart contracts a sequence learning approach to detecting security threats"
    - "vandal a scalable security analysis framework for smart contracts"
    - "securify practical security analysis of smart contracts"

Connected through hyperedge HE-6 (induced by source paper "soliditycheck quickly detecting smart contract problems through regular expressions"):
  with members:
    - "safevm a safety verifier for ethereum smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "empirical vulnerability analysis of automated smart contracts security testing on blockchains"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "towards safer smart contracts a sequence learning approach to detecting security threats"
    - "securify practical security analysis of smart contracts"
    - "under optimized smart contracts devour your money"

Connected through hyperedge HE-7 (induced by source paper "a data science approach for honeypot detection in ethereum"):
  with members:
    - "the art of the scam demystifying honeypots in ethereum smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "securify practical security analysis of smart contracts"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "xgboost a scalable tree boosting system"

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)