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: "how user throughput depends on the traffic demand in large cellular networks"
  Abstract: Little's law allows to express the mean user throughput in any region of the network as the ratio of the mean traffic demand to the steady-state mean number of users in this region. Corresponding statistics are usually collected in operational networks for each cell. Using ergodic arguments and Palm theoretic formalism, we show that the global mean user throughput in the network is equal to the ratio of these two means in the steady state of the "typical cell". Here, both means account for double averaging: over time and network geometry, and can be related to the per-surface traffic demand, base-station density and the spatial distribution of the SINR. This latter accounts for network irregularities, shadowing and idling cells via cell-load equations. We validate our approach comparing analytical and simulation results for Poisson network model to real-network cell-measurements.

Connected through hyperedge HE-1 (induced by source paper "statistical learning of geometric characteristics of wireless networks"):
  with members:
    - "a robust machine learning method for cell load approximation in wireless networks"
    - "super resolution with deep convolutional sufficient statistics"
    - "analysis of cell load coupling for lte network planning and optimization"

Connected through hyperedge HE-2 (induced by source paper "accurate characterization of dynamic cell load in noise limited random cellular networks"):
  with members:
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "joint resource partitioning and offloading in heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "cooperative transmissions in ultra dense networks under a bounded dual slope path loss model"):
  with members:
    - "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "downlink cellular network analysis with multi slope path loss models"
    - "asymptotic behavior of ultra dense cellular networks and its economic impact"
    - "will the area spectral efficiency monotonically grow as small cells go dense"
    - "downlink capacity and base station density in cellular networks"

Connected through hyperedge HE-4 (induced by source paper "performance laws of large heterogeneous cellular networks"):
  with members:
    - "wireless networks appear poissonian due to strong shadowing"
    - "what frequency bandwidth to run cellular network in a given country a downlink dimensioning problem"
    - "equivalence and comparison of heterogeneous cellular networks"
    - "analysis of cell load coupling for lte network planning and optimization"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"
    - "multi tier network performance analysis using a shotgun cellular system"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-5 (induced by source paper "what frequency bandwidth to run cellular network in a given country a downlink dimensioning problem"):
  with members:
    - "performance laws of large heterogeneous cellular networks"
    - "equivalence and comparison of heterogeneous 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)