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: "pomdps make better hackers accounting for uncertainty in penetration testing"
  Abstract: Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic testing. A key question is how to generate the attacks. This is naturally formulated as planning under uncertainty, i.e., under incomplete knowledge about the network configuration. Previous work uses classical planning, and requires costly pre-processes reducing this uncertainty by extensive application of scanning methods. By contrast, we herein model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This allows to reason about the knowledge available, and to intelligently employ scanning actions as part of the attack. As one would expect, this accurate solution does not scale. We devise a method that relies on POMDPs to find good attacks on individual machines, which are then composed into an attack on the network as a whole. This decomposition exploits network structure to the extent possible, making targeted approximations (only) where needed. Evaluating this method on a suitably adapted industrial test suite, we demonstrate its effectiveness in both runtime and solution quality.

Connected through hyperedge HE-1 (induced by source paper "autonomous intelligent cyber defense agent aica reference architecture release 2 0"):
  with members:
    - "initial reference architecture of an intelligent autonomous agent for cyber defense"
    - "toward intelligent autonomous agents for cyber defense report of the 2017 workshop by the north atlantic treaty organization nato research group ist 152 rtg"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "playing atari with deep reinforcement learning"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "an algorithm to find optimal attack paths in nondeterministic scenarios"

Connected through hyperedge HE-2 (induced by source paper "autonomous penetration testing using reinforcement learning"):
  with members:
    - "simulated penetration testing and mitigation analysis"
    - "penetration testing pomdp solving"
    - "the arcade learning environment an evaluation platform for general agents"
    - "automated attack planning"
    - "playing atari with deep reinforcement learning"
    - "simulating cyber attacks for fun and profit"

Connected through hyperedge HE-3 (induced by source paper "attack graph obfuscation"):
  with members:
    - "penetration testing pomdp solving"
    - "the fast downward planning system"

Connected through hyperedge HE-4 (induced by source paper "towards automated network mitigation analysis extended"):
  with members:
    - "attack planning in the real world"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "an algorithm to find optimal attack paths in nondeterministic scenarios"
    - "on line planning and scheduling an application to controlling modular printers"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-5 (induced by source paper "towards realistic threat modeling attack commodification irrelevant vulnerabilities and unrealistic assumptions"):
  with members:
    - "economic factors of vulnerability trade and exploitation"
    - "attack potential in impact and complexity"
    - "comparing vulnerability severity and exploits using case control studies"
    - "attack planning in the real world"

Connected through hyperedge HE-6 (induced by source paper "automated attack planning"):
  with members:
    - "les pomdp font de meilleurs hackers tenir compte de l incertitude dans les tests de penetration"
    - "attack planning in the real world"
    - "heuristic search value iteration for pomdps"
    - "deterministic pomdps revisited"
    - "simulating cyber attacks for fun and profit"
    - "online planning algorithms for pomdps"
    - "probabilistic planning via heuristic forward search and weighted model counting"
    - "perseus randomized point based value iteration for pomdps"

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.AI(Artificial Intelligence)