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: "the random oracle methodology revisited"
  Abstract: We take a critical look at the relationship between the security of cryptographic schemes in the Random Oracle Model, and the security of the schemes that result from implementing the random oracle by so called "cryptographic hash functions". The main result of this paper is a negative one: There exist signature and encryption schemes that are secure in the Random Oracle Model, but for which any implementation of the random oracle results in insecure schemes. #R##N#In the process of devising the above schemes, we consider possible definitions for the notion of a "good implementation" of a random oracle, pointing out limitations and challenges.

Connected through hyperedge HE-1 (induced by source paper "on function description"):
  with members:
    - "one way function candidate based on the collatz problem"

Connected through hyperedge HE-2 (induced by source paper "a survey of distributed consensus protocols for blockchain networks"):
  with members:
    - "deconstructing blockchains a comprehensive survey on consensus membership and structure"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "scalable bft consensus mechanism through aggregated signature gossip"
    - "asymmetric distributed trust"
    - "decentralization in bitcoin and ethereum networks"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "scaling nakamoto consensus to thousands of transactions per second"
    - "correctness and fairness of tendermint core blockchains"

Connected through hyperedge HE-3 (induced by source paper "anomigan generative adversarial networks for anonymizing private medical data"):
  with members:
    - "security and privacy issues in deep learning"
    - "technical privacy metrics a systematic survey"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "cryptodl deep neural networks over encrypted data"
    - "deep learning in bioinformatics"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "security of the fiat shamir transformation in the quantum random oracle model"):
  with members:
    - "the measure and reprogram technique 2 0 multi round fiat shamir and more"
    - "quantum lightning never strikes the same state twice"

Connected through hyperedge HE-5 (induced by source paper "merkle s key agreement protocol is optimal an o n 2 attack on any key agreement from random oracles"):
  with members:
    - "limits of random oracles in secure computation"

Connected through hyperedge HE-6 (induced by source paper "how to end password reuse on the web"):
  with members:
    - "empirical analysis of password reuse and modification across online service"
    - "the circle game scalable private membership test using trusted hardware"

Connected through hyperedge HE-7 (induced by source paper "rehand secure region based fast handover with user anonymity for small cell networks in 5g"):
  with members:
    - "emerging technologies and research challenges for 5g wireless networks"

Connected through hyperedge HE-8 (induced by source paper "dna steganalysis using deep recurrent neural networks"):
  with members:
    - "on the statistical detection of adversarial examples"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "using non invertible data transformations to build adversarial robust neural networks"
    - "deep learning in bioinformatics"
    - "learning the number of neurons in deep networks"
    - "dna level splice junction prediction using deep recurrent neural networks"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)