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: "timely throughput optimal scheduling with prediction"
  Abstract: Motivated by the increasing importance of providing delay-guaranteed services in general computing and communication systems, and the recent wide adoption of learning and prediction in network control, in this work, we consider a general stochastic single-server multi-user system and investigate the fundamental benefit of predictive scheduling in improving timely-throughput, being the rate of packets that are delivered to destinations before their deadlines. By adopting an error rate-based prediction model, we first derive a Markov decision process (MDP) solution to optimize the timely-throughput objective subject to an average resource consumption constraint. Based on a packet-level decomposition of the MDP, we explicitly characterize the optimal scheduling policy and rigorously quantify the timely-throughput improvement due to predictive-service, which scales as $\Theta(p\left[C_{1}\frac{(a-a_{\max}q)}{p-q}\rho^{\tau}+C_{2}(1-\frac{1}{p})\right](1-\rho^{D}))$, where $a, a_{\max}, \rho\in(0, 1), C_1>0, C_2\ge0$ are constants, $p$ is the true-positive rate in prediction, $q$ is the false-negative rate, $\tau$ is the packet deadline and $D$ is the prediction window size. We also conduct extensive simulations to validate our theoretical findings. Our results provide novel insights into how prediction and system parameters impact performance and provide useful guidelines for designing predictive low-latency control algorithms.

Connected through hyperedge HE-1 (induced by source paper "minimizing age of information with power constraints multi user opportunistic scheduling in multi state time varying channels"):
  with members:
    - "an aoi optimal scheduling method for wireless transmissions with truncated channel inversion"
    - "minimizing the age of information noma or oma"
    - "scheduling to minimize age of synchronization in wireless broadcast networks with random updates"
    - "scheduling to minimize age of information in multi state time varying networks with power constraints"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "reliable transmission of short packets through queues and noisy channels under latency and peak age violation guarantees"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"

Connected through hyperedge HE-2 (induced by source paper "minimizing age of information with power constraints opportunistic scheduling in multi state time varying networks"):
  with members:
    - "scheduling to minimize age of information in multi state time varying networks with power constraints"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "reliable transmission of short packets through queues and noisy channels under latency and peak age violation guarantees"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "sending information through status updates"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "scheduling policies for age minimization in wireless networks with unknown channel state"
    - "average age of information with hybrid arq under a resource constraint"

Connected through hyperedge HE-3 (induced by source paper "scheduling to minimize age of information in multi state time varying networks with power constraints"):
  with members:
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "joint queue aware and channel aware delay optimal scheduling of arbitrarily bursty traffic over multi state time varying channels"
    - "sending information through status updates"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "scheduling policies for age minimization in wireless networks with unknown channel state"
    - "average age of information with hybrid arq under a resource constraint"
    - "opportunistic scheduling as restless bandits"

Connected through hyperedge HE-4 (induced by source paper "predictive scheduling for virtual reality"):
  with members:
    - "adaptive 360 vr video streaming divide and conquer"

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)