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 "to warn or not to warn online signaling in audit games"):
  Abstract: Routine operational use of sensitive data is commonly governed by laws and regulations. For instance, in the medical domain, there are various laws at the state and federal level that dictate who is permitted to work with patients' records and under what conditions. To screen for potential privacy breaches, logging systems typically trigger alerts whenever a suspicious access is detected. However, the efficiency of such mechanism is low because 1) the vast majority of alerts are false positive and 2) limited budget leads to a small chance of detecting the true attack. To improve efficiency, information systems may invoke signaling so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. At the end of some period, a selected subset of suspicious accesses is then audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the event of data request will be audited. In this work, we formulate this problem as a Signaling Audit Game (SAG), study the properties of its Stackelberg equilibria, and develop a scalable approach to compute its solution. We show that an intelligent presentation of warnings adds value in that SAGs can realize significantly higher utility for the auditor than systems without signaling. We perform an analysis over 10 million real access events in a large academic medical center to illustrate the value of our approach.

Includes members:
  Member "get your workload in order game theoretic prioritization of database auditing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding needles in a moving haystack prioritizing alerts with adversarial reinforcement learning")
  Member "algorithmic bayesian persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of contracts")
  Member "audit games with multiple defender resources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding needles in a moving haystack prioritizing alerts with adversarial reinforcement learning")

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)