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 "near optimal blacklisting"):
  Abstract: Many applications involve agents sharing a resource, such as networks or services. When agents are honest, the system functions well and there is a net profit. Unfortunately, some agents may be malicious, but it may be hard to detect them. We consider the intrusion response problem of how to permanently blacklist agents, in order to maximise expected profit. This is not trivial, as blacklisting may erroneously expel honest agents. Conversely, while we gain information by allowing an agent to remain, we may incur a cost due to malicious behaviour. We present an efficient algorithm (HIPER) for making near-optimal decisions for this problem. Additionally, we derive three algorithms by reducing the problem to a Markov decision process (MDP). Theoretically, we show that HIPER is near-optimal. Experimentally, its performance is close to that of the full MDP solution, when the (stronger) requirements of the latter are met.

Includes members:
  Member "a game theoretical approach for finding optimal strategies in an intruder classification game":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "securing infrastructure facilities when does proactive defense help")
  Member "dynamic intrusion detection in resource constrained cyber networks":
    also connected through other hyperedges:
      (none)
  Member "complexity of stochastic branch and bound methods for belief tree search in bayesian reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monte carlo utility estimates for bayesian reinforcement learning")
  Member "towards an effective intrusion response engine combined with intrusion detection in ad hoc networks":
    also connected through other hyperedges:
      (none)
  Member "intrusion detection using cost sensitive classification":
    also connected through other hyperedges:
      (none)
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")

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)