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: "stability and capacity regions or discrete time queueing networks"
  Abstract: We consider stability and network capacity in discrete time queueing systems. Relationships between four common notions of stability are described. Specifically, we consider rate stability, mean rate stability, steady state stability, and strong stability. We then consider networks of queues with random events and control actions that can be implemented over time to affect arrivals and service at the queues. The control actions also generate a vector of additional network attributes. We characterize the network capacity region, being the closure of the set of all rate vectors that can be supported subject to network stability and to additional time average attribute constraints. We show that (under mild technical assumptions) the capacity region is the same under all four stability definitions. Our capacity achievability proof uses the drift-plus-penalty method of Lyapunov optimization, and provides full details for the case when network states obey a decaying memory property, which holds for finite state ergodic systems and more general systems.

Connected through hyperedge HE-1 (induced by source paper "predictive control and communication co design a gaussian process regression approach"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "taming the tail of maximal information age in wireless industrial networks"
    - "wireless network intelligence at the edge"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "wireless network design for control systems a survey"

Connected through hyperedge HE-2 (induced by source paper "optimal network control in partially controllable networks"):
  with members:
    - "value prediction network"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "a distributed algorithm for throughput optimal routing in overlay networks"
    - "value iteration networks"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "maximum lifetime analytics in iot networks"):
  with members:
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "delay optimal scheduling for queueing systems with switching overhead"):
  with members:
    - "towards provably safe mixed transportation systems with human driven and automated vehicles"

Connected through hyperedge HE-5 (induced by source paper "stochastic online control for energy harvesting wireless networks with battery imperfections"):
  with members:
    - "energy harvesting broadband communication systems with processing energy cost"
    - "throughput maximization for the gaussian relay channel with energy harvesting constraints"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "optimal scheduling and power allocation for two hop energy harvesting communication systems"
    - "throughput optimal policies for energy harvesting wireless transmitters with non ideal circuit power"
    - "optimal save then transmit protocol for energy harvesting wireless transmitters"
    - "broadcasting with an energy harvesting rechargeable transmitter"
    - "sum rate optimal power policies for energy harvesting transmitters in an interference channel"

Connected through hyperedge HE-6 (induced by source paper "two scale stochastic control for multipoint communication systems with renewables"):
  with members:
    - "power delay tradeoff with predictive scheduling in integrated cellular and wi fi networks"
    - "decentralized energy allocation for wireless networks with renewable energy powered base stations"
    - "cooperative energy trading in comp systems powered by smart grids"
    - "group sparse beamforming for green cloud ran"
    - "optimal power cost management using stored energy in data centers"
    - "linear precoding in cooperative mimo cellular networks with limited coordination clusters"
    - "networked mimo with clustered linear precoding"

Connected through hyperedge HE-7 (induced by source paper "regret of queueing bandits"):
  with members:
    - "combinatorial semi bandit with known covariance"
    - "combinatorial bandits revisited"
    - "learning to optimize via posterior sampling"
    - "regret bounds for restless markov bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-8 (induced by source paper "capacity regions of two receiver broadcast erasure channels with feedback and memory"):
  with members:
    - "capacity regions of two user broadcast erasure channels with feedback and hidden memory"
    - "an achievable rate region for the broadcast channel with feedback"
    - "multiuser broadcast erasure channel with feedback capacity and algorithms"
    - "fundamental limits of caching"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

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)