human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a decision tree learning approach for mining relationship based access control policies"):
  Abstract: Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing, by allowing policies to be expressed in terms of chains of relationships between entities. ReBAC policy mining algorithms have the potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy. #R##N#This paper presents a new algorithm, based on decision trees, for mining ReBAC policies from access control lists (ACLs) and information about entities. The algorithm first learns an authorization policy in the form of a decision tree, and then extracts a set of candidate authorization rules from the decision tree. Next, it constructs the final mined policy by eliminating negative conditions from the candidate rules and then simplifying them. Compared to state-of-the-art ReBAC mining algorithms, DTRM is simpler, significantly faster, achieves similar policy quality, and can mine policies in a richer language.

Includes members:
  Member "the next 700 policy miners a universal method for building policy miners":
    also connected through other hyperedges:
      (none)
  Member "efficient and extensible policy mining for relationship based access control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "contextual and granular policy enforcement in database backed applications")
  Member "mining attribute based access control policies from logs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "contextual and granular policy enforcement in database backed applications")
  Member "mining attribute based access control policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual and granular policy enforcement in database backed applications")

Question: Please tell me which class the source paper 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)