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: "integrating privacy enhancing techniques into blockchains using sidechains"
  Abstract: Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockchain makes it a winner on security and transparency; it is nearly inconceivable for ledgers to be altered in a way not instantly clear to every single user involved. However, most blockchains fall short in privacy aspects, particularly in data protection. Garlic Routing and Onion Routing are two of major Privacy Enhancing Techniques (PETs) which are popular for anonymization and security. Garlic Routing is a methodology using by I2P Anonymous Network to hide the identity of sender and receiver of data packets by bundling multiple messages into a layered encryption structure. The Onion Routing attempts to provide lowlatency Internet-based connections that resist traffic analysis, deanonymization attack, eavesdropping, and other attacks both by outsiders (e.g. Internet routers) and insiders (Onion Routing servers themselves). As there are a few controversies over the rate of resistance of these two techniques to privacy attacks, we propose a PET-Enabled Sidechain (PETES) as a new privacy enhancing technique by integrating Garlic Routing and Onion Routing into a Garlic Onion Routing (GOR) framework suitable to the structure of blockchains. The preliminary proposed GOR aims to improve the privacy of transactions in blockchains via PETES structure.

Connected through hyperedge HE-1 (induced by source paper "blockchain applications in power systems a bibliometric analysis"):
  with members:
    - "smart grid cyber attacks detection using supervised learning and heuristic feature selection"
    - "a secure and efficient direct power load control framework based on blockchain"
    - "review of blockchain technology and its expectations case of the energy sector"
    - "the journal coverage of web of science and scopus a comparative analysis"
    - "network function virtualization state of the art and research challenges"

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)