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 unreasonable effectiveness of address clustering"
  Abstract: Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe the primary reasons behind this effectiveness: address reuse, avoidable merging, super-clusters with high centrality,, the incremental growth of address clusters. We quantify their impact during Bitcoin's first seven years of existence.

Connected through hyperedge HE-1 (induced by source paper "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "scalable graph learning for anti money laundering a first look"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "a deep dive into bitcoin mining pools an empirical analysis of mining shares"):
  with members:
    - "decentralization in bitcoin and ethereum networks"
    - "estimation of miner hash rates and consensus on blockchains draft"
    - "bitcoin s security model revisited"
    - "optimal selfish mining strategies in bitcoin"
    - "analysis of hashrate based double spending"
    - "on subversive miner strategies and block withholding attack in bitcoin digital currency"
    - "the miner s dilemma"
    - "majority is not enough bitcoin mining is vulnerable"

Connected through hyperedge HE-3 (induced by source paper "safeguarding the evidential value of forensic cryptocurrency investigations"):
  with members:
    - "ransomware payments in the bitcoin ecosystem"
    - "an empirical analysis of anonymity in zcash"
    - "an empirical analysis of linkability in the monero blockchain"
    - "blocksci design and applications of a blockchain analysis platform"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies"

Connected through hyperedge HE-4 (induced by source paper "spams meet cryptocurrencies sextortion in the bitcoin ecosystem"):
  with members:
    - "ransomware payments in the bitcoin ecosystem"
    - "blocksci design and applications of a blockchain analysis platform"
    - "economic analysis of ransomware"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-5 (induced by source paper "visualizing and analyzing entity activity on the bitcoin network"):
  with members:
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "bitcoin transaction graph analysis"

Connected through hyperedge HE-6 (induced by source paper "probing the mystery of cryptocurrency theft an investigation into methods for taint analysis"):
  with members:
    - "bitcoin transaction graph analysis"

Connected through hyperedge HE-7 (induced by source paper "characterizing entities in the bitcoin blockchain"):
  with members:
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "ransomware payments in the bitcoin ecosystem"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "system g distributed graph database"
    - "when a small leak sinks a great ship deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "blockchain a graph primer"
    - "link prediction by de anonymization how we won the kaggle social network challenge"

Connected through hyperedge HE-8 (induced by source paper "a probabilistic model of the bitcoin blockchain"):
  with members:
    - "characterizing entities in the bitcoin blockchain"
    - "data mining for detecting bitcoin ponzi schemes"
    - "ransomware payments in the bitcoin ecosystem"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "blockchain a graph primer"
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)