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 "markov decision processes with applications in wireless sensor networks a survey"):
  Abstract: Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are used to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

Includes members:
  Member "distributed and centralized hybrid csma ca tdma schemes for single hop wireless networks":
    also connected through other hyperedges:
      (none)
  Member "machine learning in wireless sensor networks algorithms strategies and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "markov decision processes with multiple long run averageobjectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")
  Member "on the complexity of solving markov decision problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "internet of things iot a vision architectural elements and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "delay optimal data forwarding in vehicular sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multihop routing for data delivery in v2x networks")
  Member "sensor scheduling for energy efficient target tracking in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "novel sensor scheduling scheme for intruder tracking in energy efficient sensor networks")
  Member "sleep control for tracking in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved active sensing performance in wireless sensor networks via channel state information extended version")

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.NI(Networking and Internet Architecture)