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: "bounds on the capacity of ofdm underspread frequency selective fading channels"
  Abstract: The analysis of the channel capacity in the absence of prior channel knowledge (noncoherent channel) has gained increasing interest in recent years, but it is still unknown for the general case. In this paper we derive bounds on the capacity of the noncoherent, underspread complex Gaussian, orthogonal frequency division multiplexing (OFDM), wide sense stationary channel with uncorrelated scattering (WSSUS), under a peak power constraint or a constraint on the second and fourth moments of the transmitted signal. These bounds are characterized only by the system signal-to-noise ratio (SNR) and by a newly defined quantity termed effective coherence time. Analysis of the effective coherence time reveals that it can be interpreted as the length of a block in the block fading model in which a system with the same SNR will achieve the same capacity as in the analyzed channel. Unlike commonly used coherence time definitions, it is shown that the effective coherence time depends on the SNR, and is a nonincreasing function of it. We show that for low SNR the capacity is proportional to the effective coherence time, while for higher SNR the coherent channel capacity can be achieved provided that the effective coherence time is large enough.

Connected through hyperedge HE-1 (induced by source paper "uplink downlink rate balancing in cooperating cellular networks"):
  with members:
    - "a lower bound on the data rate of dirty paper coding in general noise and interference"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "fundamental limits of cooperation"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "multi user diversity vs accurate channel state information in mimo downlink channels"
    - "massive mimo how many antennas do we need"
    - "completely stale transmitter channel state information is still very useful"
    - "interference alignment with analog channel state feedback"

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.IT(Information Theory)