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: "throughput optimal scheduling design with regular service guarantees in wireless networks"
  Abstract: Motivated by the regular service requirements of video applications for improving quality of experience (QoE) of users, we consider the design of scheduling strategies in multihop wireless networks that not only maximize system throughput but also provide regular interservice times for all links. Since the service regularity of links is related to the higher-order statistics of the arrival process and the policy operation, it is challenging to characterize and analyze directly. We overcome this obstacle by introducing a new quantity, namely the time-since-last-service (TSLS), which tracks the time since the last service. By combining it with the queue length in the weight, we propose a novel maximum-weight-type scheduling policy, called Regular Service Guarantee (RSG) Algorithm. The unique evolution of the TSLS counter poses significant challenges for the analysis of the RSG Algorithm. To tackle these challenges, we first propose a novel Lyapunov function to show the throughput optimality of the RSG Algorithm. Then, we prove that the RSG Algorithm can provide service regularity guarantees by using the Lyapunov-drift-based analysis of the steady-state behavior of the stochastic processes. In particular, our algorithm can achieve a degree of service regularity within a factor of a fundamental lower bound we derive. This factor is a function of the system statistics and design parameters and can be as low as two in some special networks. Our results, both analytical and numerical, exhibit significant service regularity improvements over the traditional throughput-optimal policies, which reveals the importance of incorporating the metric of time-since-last-service into the scheduling policy for providing regulated service.

Connected through hyperedge HE-1 (induced by source paper "benefits of coding on age of information in broadcast networks"):
  with members:
    - "sampling for remote estimation through queues age of information and beyond"
    - "optimal age over erasure channels"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "the age of information in networks moments distributions and sampling"
    - "online timely status updates with erasures for energy harvesting sensors"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "delay and peak age violation probability in short packet transmissions"

Connected through hyperedge HE-2 (induced by source paper "resource allocation for loss tolerant video streaming in embms"):
  with members:
    - "towards optimal grouping and resource allocation for multicast streaming in lte"
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "optimized network coded scalable video multicasting over embms networks"

Connected through hyperedge HE-3 (induced by source paper "scheduling policies for minimizing age of information in broadcast wireless networks"):
  with members:
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "age optimal information updates in multihop networks"
    - "age and value of information non linear age case"
    - "status updates over unreliable multiaccess channels"
    - "update or wait how to keep your data fresh"
    - "age of information in the presence of error"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "age of information the gamma awakening"

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)