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 "spectral efficiency scaling laws in dense random wireless networks with multiple receive antennas"):
  Abstract: This paper considers large random wireless networks where transmit-and-receive node pairs communicate within a certain range while sharing a common spectrum. By modeling the spatial locations of nodes based on stochastic geometry, analytical expressions for the ergodic spectral efficiency of a typical node pair are derived as a function of the channel state information available at a receiver (CSIR) in terms of relevant system parameters: the density of communication links, the number of receive antennas, the path loss exponent, and the operating signal-to-noise ratio. One key finding is that when the receiver only exploits CSIR for the direct link, the sum of spectral efficiencies linearly improves as the density increases, when the number of receive antennas increases as a certain super-linear function of the density. When each receiver exploits CSIR for a set of dominant interfering links in addition to the direct link, the sum of spectral efficiencies linearly increases with both the density and the path loss exponent if the number of antennas is a linear function of the density. This observation demonstrates that having CSIR for dominant interfering links provides a multiplicative gain in the scaling law. It is also shown that this linear scaling holds for direct CSIR when incorporating the effect of the receive antenna correlation, provided that the rank of the spatial correlation matrix scales super-linearly with the density. Simulation results back scaling laws derived from stochastic geometry.

Includes members:
  Member "a geometrical based throughput bound analysis for device to device communications in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "connectivity of underlay cognitive radio networks with directional antennas")
  Member "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "downlink cellular network analysis with multi slope path loss models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "the performance of successive interference cancellation in random wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sir analysis via signal fractions")
  Member "on the optimality of treating interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")
  Member "power control for d2d underlaid cellular networks modeling algorithms and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "multi antenna communication in ad hoc networks achieving mimo gains with simo transmission":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "spatial interference cancellation for multi antenna mobile ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalized interference alignment part ii application to wireless secrecy")

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)