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: "modeling and performance analysis in cache enabled millimeter wave hetnets with access and backhaul integration"
  Abstract: Recently, a mmWave-based access and backhaul integration HetNetsarchitecture (mABHetNets) has been envisioned to provide the high link capacity in both access and backhaul links. In mABHetNets, the access link and the backhaul link will share the same mm-wave spectral resources to serve cellular users as well as to maintain the backhaul capacity of the SBSs. However, a large spectrum bandwidth are occupied by the backhaul link to maintain the backhaul link capacity. Up to 50\% mmWave spectrum resource will be used in backhaul link to satisfy the high speed data traffic. Such "\emph{spectrum occupation}" of backhaul link has restricted network performance to achieve a better possible improvement. To overcome spectrum occupation problem in the existing mABHetNets, involving caches in the mABHetNets brings a opportunity. When the backhaul traffic is offloaded by caching popular fils in the SBSs and the corresponding mmWave spectrum can be transfered to the access link by the bandwidth partition. Then with more spectrum for access link, both the network throughout and the coverage ability are increased significantly. However, the caching power consumption will be increased and the EE will be decreased. Therefore, we analyze the througput and EE in mABHetNets by stochastic geometry tool. Extensive numerical and simulations results are carried out to validate our work.

Connected through hyperedge HE-1 (induced by source paper "throughput analysis in cache enabled millimeter wave hetnets with access and backhaul integration"):
  with members:
    - "a unified asymptotic analysis of area spectral efficiency in ultradense cellular networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "energy efficiency of downlink networks with caching at base stations"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "downlink cellular network analysis with multi slope path loss models"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "analysis of blockage effects on urban cellular networks"

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)