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: "mining attribute based access control policies"
  Abstract: Attribute-based access control (ABAC) provides a high level of flexibility that promotes security and information sharing. ABAC policy mining algorithms have potential to significantly reduce the cost of migration to ABAC, by partially automating the development of an ABAC policy from an access control list (ACL) policy or role-based access control (RBAC) policy with accompanying attribute data. This paper presents an ABAC policy mining algorithm. To the best of our knowledge, it is the first ABAC policy mining algorithm. Our algorithm iterates over tuples in the given user-permission relation, uses selected tuples as seeds for constructing candidate rules, and attempts to generalize each candidate rule to cover additional tuples in the user-permission relation by replacing conjuncts in attribute expressions with constraints. Our algorithm attempts to improve the policy by merging and simplifying candidate rules, and then it selects the highest-quality candidate rules for inclusion in the generated policy.

Connected through hyperedge HE-1 (induced by source paper "contextual and granular policy enforcement in database backed applications"):
  with members:
    - "lweb information flow security for multi tier web applications"
    - "efficient and extensible policy mining for relationship based access control"
    - "precise dynamic information flow for database backed applications"
    - "mining attribute based access control policies from logs"

Connected through hyperedge HE-2 (induced by source paper "an automatic attribute based access control policy extraction from access logs"):
  with members:
    - "mining attribute based access control policies from logs"

Connected through hyperedge HE-3 (induced by source paper "efficient and extensible policy mining for relationship based access control"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "greedy and evolutionary algorithms for mining relationship based access control policies"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "mining attribute based access control policies from logs"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "a decision tree learning approach for mining relationship based access control policies"):
  with members:
    - "the next 700 policy miners a universal method for building policy miners"
    - "efficient and extensible policy mining for relationship based access control"
    - "mining attribute based access control policies from logs"

Connected through hyperedge HE-5 (induced by source paper "the next 700 policy miners a universal method for building policy miners"):
  with members:
    - "greedy and evolutionary algorithms for mining relationship based access control policies"
    - "mining hierarchical temporal roles with multiple metrics"
    - "mining attribute based access control policies from logs"

Connected through hyperedge HE-6 (induced by source paper "greedy and evolutionary algorithms for mining relationship based access control policies"):
  with members:
    - "mining hierarchical temporal roles with multiple metrics"
    - "mining attribute based access control policies from logs"
    - "path conditions and principal matching a new approach to access control"

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)