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 "spatio temporal network dynamics framework for energy efficient ultra dense cellular networks"):
  Abstract: This article investigates the performance of an ultra-dense network (UDN) from an energy-efficiency (EE) standpoint leveraging the interplay between stochastic geometry (SG) and mean-field game (MFG) theory. In this setting, base stations (BSs) (resp. users) are uniformly distributed over a two-dimensional plane as two independent homogeneous Poisson point processes (PPPs), where users associate to their nearest BSs. The goal of every BS is to maximize its own energy efficiency subject to channel uncertainty, random BS location, and interference levels. Due to the coupling in interference, the problem is solved in the mean-field (MF) regime where each BS interacts with the whole BS population via time-varying MF interference. As a main contribution, the asymptotic convergence of MF interference to zero is rigorously proved in a UDN with multiple transmit antennas. It allows us to derive a closed-form EE representation, yielding a tractable EE optimal power control policy. This proposed power control achieves more than 1.5 times higher EE compared to a fixed power baseline.

Includes members:
  Member "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "energy efficient resource management in ultra dense small cell networks a mean field approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time complexity analysis of a distributed stochastic optimization in a non stationary environment")
  Member "on coordinating ultra dense wireless access networks optimization modeling algorithms and insights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "analysis of blockage effects on urban cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting randomly located blockages for large scale deployment of intelligent surfaces")
  Member "coverage and economy of cellular networks with many base stations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coverage analysis of 3 d dense cellular networks with realistic propagation conditions")
  Member "adaptive training for correlated fading channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics")
  Member "downlink capacity and base station density in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ran slicing for massive iot and bursty urllc service multiplexing analysis and optimization")

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.IT(Information Theory)