human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "delay optimal biased user association in heterogeneous networks"):
  Abstract: In heterogeneous networks (HetNets), load balancing among different tiers can be effectively achieved by a biased user association scheme with which each user chooses to associate with one base station (BS) based on the biased received power. In contrast to previous studies where a BS always has packets to transmit, we assume in this paper that incoming packets intended for all the associated users form a queue in the BS. In order to find the delay limit of the network to support real-time service, we focus on the delay optimization problem by properly tuning the biasing factor of each tier. By adopting a thinned Poisson point process (PPP) model to characterize the locations of BSs in the busy state, an explicit expression of the average traffic intensity of each tier is obtained. On that basis, an optimization problem is formulated to minimize a lower bound of the network mean queuing delay. By showing that the optimization problem is convex, the optimal biasing factor of each tier can be obtained numerically. When the mean packet arrival rate of each user is small, a closed-form solution is derived. The simulation results demonstrate that the network queuing performance can be significantly improved by properly tuning the biasing factor. It is further shown that the network mean queuing delay might be improved at the cost of a deterioration of the network signal-to-interference ratio (SIR) coverage, which indicates a performance tradeoff between real-time and non-real-time traffic in HetNets.

Includes members:
  Member "random cell association and void probability in poisson distributed cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mmwave uav networks with multi cell association performance limit and optimization")
  Member "optimizing user association and spectrum allocation in hetnets a utility perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms")
  Member "optimal base station deployment for small cell networks with energy efficient power control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamentals of the downlink green coverage and energy efficiency in heterogeneous networks")
  Member "traffic driven spectrum allocation in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimization of mimo device to device networks via matrix fractional programming a minorization maximization approach")
  Member "an overview of load balancing in hetnets old myths and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "joint resource partitioning and offloading in heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights")
  Member "offloading in heterogeneous networks modeling analysis and design insights":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come")

Question: Please tell me which class the source paper 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)