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: "distributed double spending prevention"
  Abstract: We study the problem of preventing double spending in electronic payment schemes in a distributed fashion. This problem occurs, for instance, when the spending of electronic coins needs to be controlled by a large collection of nodes (eg. in a peer-to-peer (P2P) system) instead of one central bank. Contrary to the commonly held belief that this is fundamentally impossible, we propose several solutions that do achieve a reasonable level of double spending prevention, and analyse their efficiency under varying assumptions.

Connected through hyperedge HE-1 (induced by source paper "trustable and automated machine learning running with blockchain and its applications"):
  with members:
    - "a unified analytical framework for trustable machine learning and automation running with blockchain"
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dynamic routing between capsules"
    - "toward geometric deep slam"
    - "xgboost a scalable tree boosting system"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a unified analytical framework for trustable machine learning and automation running with blockchain"):
  with members:
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"

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)