human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "privacy preserving blockchain mining sybil resistance by proof of useful work"):
  Abstract: Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work (PoW) provides a solution by linking representation to a valuable, physical resource. This has worked well, currently securing Bitcoins $100 B value. However, the Bitcoin network uses a tremendous amount of specialized hardware and energy, and since the utility of these resources is strictly limited to blockchain security, the resources used are not useful other purposes. Here, we propose an alternative consensus scheme that directs the computational resources to a task with utility beyond blockchain security, aiming at better resource utilization. The key idea is to channel the resources to optimization of machine learning (ML) models by setting up decentralized ML competitions. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.

Includes members:
  Member "secure computation for machine learning with spdz":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy for free communication efficient learning with differential privacy using sketches")
  Member "a scalable verification solution for blockchains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "dfinity technology overview series consensus system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trustable and automated machine learning running with blockchain and its applications")
  Member "consensus in the age of blockchains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hushrelay a privacy preserving efficient and scalable routing algorithm for off chain payments")
  Member "enhancing bitcoin security and performance with strong consistency via collective signing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "bitcoin ng a scalable blockchain protocol":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "enigma decentralized computation platform with guaranteed privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an overview on smart contracts challenges advances and platforms")

Question: Please tell me which class the source paper 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)