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: "characterizing entities in the bitcoin blockchain"
  Abstract: Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.

Connected through hyperedge HE-1 (induced by source paper "cascading machine learning to attack bitcoin anonymity"):
  with members:
    - "data mining for detecting bitcoin ponzi schemes"
    - "anomaly detection in bitcoin network using unsupervised learning methods"
    - "bitcoin transaction graph analysis"
    - "deanonymisation of clients in bitcoin p2p network"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-2 (induced by source paper "an evaluation of bitcoin address classification based on transaction history summarization"):
  with members:
    - "data mining for detecting bitcoin ponzi schemes"
    - "a survey on security and privacy issues of bitcoin"
    - "xgboost a scalable tree boosting system"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic model of the bitcoin blockchain"):
  with members:
    - "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"
    - "the unreasonable effectiveness of address clustering"
    - "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)