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: "distributed and centralized hybrid csma ca tdma schemes for single hop wireless networks"
  Abstract: The strength of carrier-sense multiple access with collision avoidance (CSMA/CA) can be combined with that of time-division multiple access (TDMA) to enhance the channel access performance in wireless networks such as the IEEE 802.15.4-based wireless personal area networks. In particular, the performance of legacy CSMA/CA-based medium access control scheme in congested networks can be enhanced through a hybrid CSMA/CA-TDMA scheme while preserving the scalability property. In this paper, we present distributed and centralized channel access models that follow the transmission strategies based on Markov decision process (MDP) to access both contention period and contention-free period in an intelligent way. The models consider the buffer status as an indication of congestion provided that the offered traffic does not exceed the channel capacity. We extend the models to consider the hidden node collision problem encountered due to the signal attenuation caused by channel fading. The simulation results show that the MDP-based distributed channel access scheme outperforms the legacy slotted CSMA/CA scheme. The centralized model outperforms the distributed model but requires the global information of the network.

Connected through hyperedge HE-1 (induced by source paper "markov decision processes with applications in wireless sensor networks a survey"):
  with members:
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "markov decision processes with multiple long run averageobjectives"
    - "on the complexity of solving markov decision problems"
    - "internet of things iot a vision architectural elements and future directions"
    - "delay optimal data forwarding in vehicular sensor networks"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "sleep control for tracking in sensor networks"
    - "one counter markov decision processes"

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.NI(Networking and Internet Architecture)