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: "scheduling with rate adaptation under incomplete knowledge of channel estimator statistics"
  Abstract: In time-varying wireless networks, the states of the communication channels are subject to random variations, and hence need to be estimated for efficient rate adaptation and scheduling. The estimation mechanism possesses inaccuracies that need to be tackled in a probabilistic framework. In this work, we study scheduling with rate adaptation in single-hop queueing networks under two levels of channel uncertainty: when the channel estimates are inaccurate but complete knowledge of the channel/estimator joint statistics is available at the scheduler; and when the knowledge of the joint statistics is incomplete. In the former case, we characterize the network stability region and show that a maximum-weight type scheduling policy is throughput-optimal. In the latter case, we propose a joint channel statistics learning - scheduling policy. With an associated trade-off in average packet delay and convergence time, the proposed policy has a stability region arbitrarily close to the stability region of the network under full knowledge of channel/estimator joint statistics.

Connected through hyperedge HE-1 (induced by source paper "low complexity optimal scheduling over time correlated fading channels with arq feedback"):
  with members:
    - "green communication via power optimized harq protocols"
    - "exploiting hybrid channel information for downlink multi user mimo scheduling"
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "joint scheduling and resource allocation in the ofdma downlink utility maximization under imperfect channel state information"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "optimality of myopic sensing in multi channel opportunistic access"

Connected through hyperedge HE-2 (induced by source paper "scheduling in time correlated wireless networks with imperfect csi and stringent constraint"):
  with members:
    - "exploiting hybrid channel information for downlink multi user mimo scheduling"
    - "multiuser scheduling in a markov modeled downlink using randomly delayed arq feedback"
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "joint scheduling and resource allocation in the ofdma downlink utility maximization under imperfect channel state information"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"

Connected through hyperedge HE-3 (induced by source paper "joint opportunistic scheduling and selective channel feedback"):
  with members:
    - "smart scheduling and feedback allocation over non stationary wireless channels"
    - "performance and convergence of multi user online learning"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "rethinking information theory for mobile ad hoc networks"

Connected through hyperedge HE-4 (induced by source paper "asymptotically optimal downlink scheduling over markovian fading channels"):
  with members:
    - "joint scheduling and resource allocation in the ofdma downlink utility maximization under imperfect channel state information"
    - "exploiting channel memory for multi user wireless scheduling without channel measurement capacity regions and algorithms"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "approximation algorithms for restless bandit problems"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "downlink scheduling over markovian fading channels"):
  with members:
    - "asymptotically optimal downlink scheduling over markovian fading channels"
    - "joint scheduling and resource allocation in the ofdma downlink utility maximization under imperfect channel state information"
    - "exploiting channel memory for joint estimation and scheduling in downlink networks"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "approximation algorithms for restless bandit problems"
    - "reinforcement learning a survey"

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)