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: "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.

Connected through hyperedge HE-1 (induced by source paper "optimized trajectory design in uav based cellular networks for 3d users a double q learning approach"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-2 (induced by source paper "dynamic content update for wireless edge caching via deep reinforcement learning"):
  with members:
    - "a deep reinforcement learning based framework for content caching"
    - "caching at the wireless edge design aspects challenges and future directions"

Connected through hyperedge HE-3 (induced by source paper "distributed relay selection in presence of dynamic obstacles in millimeter wave d2d communication"):
  with members:
    - "network assisted d2d relay selection under the presence of dynamic obstacles"
    - "coverage and rate analysis for millimeter wave cellular networks"

Connected through hyperedge HE-4 (induced by source paper "a decentralized optimization framework for energy harvesting devices"):
  with members:
    - "battery powered devices in wpcns"
    - "decentralized transmission policies for energy harvesting devices"
    - "optimal adaptive random multiaccess in energy harvesting wireless sensor networks"
    - "energy harvesting wireless communications a review of recent advances"
    - "joint transmission and energy transfer policies for energy harvesting devices with finite batteries"
    - "universally near optimal online power control for energy harvesting nodes"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "a learning theoretic approach to energy harvesting communication system optimization"

Connected through hyperedge HE-5 (induced by source paper "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"):
  with members:
    - "distributed energy spectral efficiency optimization for partial full interference alignment in multi user multi relay multi cell mimo systems"
    - "energy efficient ofdma networks maintaining statistical qos guarantees for delay sensitive traffic"
    - "wireless sensor network virtualization a survey"
    - "accuracy of homology based coverage hole detection for wireless sensor networks on sphere"
    - "end to end reliability aware scheduling for wireless sensor networks"
    - "multiband spectrum access great promises for future cognitive radio networks"
    - "spectral and energy spectral efficiency optimization of joint transmit and receive beamforming based multi relay mimo ofdma cellular networks"
    - "achieving maximum energy efficiency in multi relay ofdma cellular networks a fractional programming approach"

Connected through hyperedge HE-6 (induced by source paper "a linearly relaxed approximate linear program for markov decision processes"):
  with members:
    - "a generalized reduced linear program for markov decision processes"
    - "feature selection using regularization in approximate linear programs for markov decision processes"
    - "efficient solution algorithms for factored mdps"

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)