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: "sleep control for tracking in sensor networks"
  Abstract: We study the problem of tracking an object moving through a network of wireless sensors. In order to conserve energy, the sensors may be put into a sleep mode with a timer that determines their sleep duration. It is assumed that an asleep sensor cannot be communicated with or woken up, and hence the sleep duration needs to be determined at the time the sensor goes to sleep based on all the information available to the sensor. Having sleeping sensors in the network could result in degraded tracking performance, therefore, there is a tradeoff between energy usage and tracking performance. We design sleeping policies that attempt to optimize this tradeoff and characterize their performance. As an extension to our previous work in this area, we consider generalized models for object movement, object sensing, and tracking cost. For discrete state spaces and continuous Gaussian observations, we derive a lower bound on the optimal energy-tracking tradeoff. It is shown that in the low tracking error regime, the generated policies approach the derived lower bound.

Connected through hyperedge HE-1 (induced by source paper "improved active sensing performance in wireless sensor networks via channel state information extended version"):
  with members:
    - "performance guarantees for adaptive estimation of sparse signals"
    - "wideband spectrum sensing for cognitive radio networks a survey"

Connected through hyperedge HE-2 (induced by source paper "markov decision processes with applications in wireless sensor networks a survey"):
  with members:
    - "distributed and centralized hybrid csma ca tdma schemes for single hop wireless networks"
    - "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"
    - "one counter markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "two timescale convergent q learning for sleep scheduling in wireless sensor networks"):
  with members:
    - "delay aware bs discontinuous transmission control and user scheduling for energy harvesting downlink coordinated mimo systems"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"

Connected through hyperedge HE-4 (induced by source paper "optimal strategies for communication and remote estimation with an energy harvesting sensor"):
  with members:
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling in an energy harvesting communication system"
    - "paging and registration in cellular networks jointly optimal policies and an iterative algorithm"

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)