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 "performance of multiantenna linear mmse receivers in doubly stochastic networks"):
  Abstract: A technique is presented to characterize the signal-to-interference-plus-noise ratio (SINR) of a representative link with a multiantenna linear minimum-mean-square-error receiver in a wireless network with transmitting nodes distributed according to a doubly stochastic process, which is a generalization of the Poisson point process. The cumulative distribution function of the SINR of the representative link is derived, assuming independent Rayleigh fading between antennas. Several representative spatial node distributions are considered, including networks with both deterministic and random clusters, strip networks (used to model roadways, for example), hardcore networks and networks with generalized path-loss models. In addition, it is shown that if the number of antennas at the representative receiver is linearly increased with the nominal node density, the signal-to-interference ratio converges in distribution to a random variable that is nonzero in general and a positive constant in certain cases. This result indicates that to the extent that the system assumptions hold, it is possible to scale such networks by increasing the number of receiver antennas linearly with the node density. The results presented here are useful in characterizing the performance of multiantenna wireless networks in more general network models than what are currently available.

Includes members:
  Member "performance of multi antenna mmse receivers in non homogenous poisson networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance of multi antenna linear mmse receivers in non homogeneous poisson and poisson cluster networks")
  Member "interference and throughput in aloha based ad hoc networks with isotropic node distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic data rates of receive diversity systems with mmse estimation and spatially correlated interferers")
  Member "high sir transmission capacity of wireless networks with general fading and node distribution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on interference dynamics in matern networks")
  Member "multi antenna communication in ad hoc networks achieving mimo gains with simo transmission":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "open loop spatial multiplexing and diversity communications in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "on the spectral efficiency of links with multi antenna receivers in non homogenous wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effect of spatial interference correlation on the performance of maximum ratio combining")
  Member "outage probability of general ad hoc networks in the high reliability regime":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure transmission in linear multihop relaying networks")
  Member "performance of optimum combining in a poisson field of interferers and rayleigh fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wireless powered asynchronous backscatter networks with sporadic short packets performance 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)