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: "social signals in the ethereum trading network"
  Abstract: Blockchain technology, which has been known by mostly small technological circles up until recently, is bursting throughout the globe, with a potential economic and social impact that could fundamentally alter traditional financial and social structures. Issuing cryptocurrencies on top of the Blockchain system by startups and private sector companies is becoming a ubiquitous phenomenon, inducing the trading of these crypto-coins among their holders using dedicated exchanges. #R##N#Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. Analyzing and modeling the dynamics of the "social signals" of this network can contribute to our understanding of this ecosystem and the forces acting within in. #R##N#This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins' trading data. Considering all trading wallets as a network's nodes, and constructing its edges using buy--sell trades, we can analyze the network properties of the ERC20 network. Examining several periods of time, and several data aggregation variants, we demonstrate that the network displays strong power-law properties. These results coincide with current network theory expectations, however nonetheless, are the first scientific validation of it, for the ERC20 trading data. #R##N#The data we examined is composed of over 30 million ERC20 tokens trades, performed by over 6.8 million unique wallets, lapsing over a two years period between February 2016 and February 2018.

Connected through hyperedge HE-1 (induced by source paper "characterizing and detecting money laundering activities on the bitcoin network"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "data mining for detecting bitcoin ponzi schemes"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "anomaly detection in the bitcoin system a network perspective"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "delay and communication tradeoffs for blockchain systems with lightweight iot clients"):
  with members:
    - "smart contract based access control for the internet of things"
    - "analysis of the communication traffic for blockchain synchronization of iot devices"
    - "distributed proportional fairness control in microgrids via blockchain smart contracts"
    - "novel market approach for locally balancing renewable energy production and flexible demand"
    - "update or wait how to keep your data fresh"
    - "massive machine type communications in 5g physical and mac layer solutions"

Connected through hyperedge HE-3 (induced by source paper "detecting token systems on ethereum"):
  with members:
    - "empirical vulnerability analysis of automated smart contracts security testing on blockchains"
    - "securify practical security analysis of smart contracts"
    - "an empirical analysis of smart contracts platforms applications and design patterns"
    - "a survey of symbolic execution techniques"

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.SI(Social and Information Networks)