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: "generalized degrees freedom of noncoherent mimo with asymmetric links"
  Abstract: We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent multiple input multiple output (MIMO) channel with asymmetric distributions of link strengths, and a coherence time of T symbol durations. We derive the optimal signaling structure for communication for asymmetric MIMO, which is distinct from that for the MIMO with identically independent distributed (i.i.d.) links. We extend the existing gDoF results for single input multiple output (SIMO) with i.i.d. links to the asymmetric case, proving that selecting the statistically best antenna is gDoF-optimal. Using the gDoF result for SIMO, we prove that for T=1, the gDoF is zero for MIMO channels with arbitrary link strength distributions, extending the result for MIMO with i.i.d. links. We show that selecting the statistically best antenna is gDoF-optimal for multiple input single output (MISO) channel. We also derive the gDoF for the 2x2 MIMO channel with different exponents in the direct and cross links. In this setting, we show that it is always necessary to use both the antennas to achieve the optimal gDoF, in contrast to the results for 2x2 MIMO with i.i.d. links. We show that having weaker crosslinks, gives gDoF gain compared to the case with i.i.d. links. For noncoherent MIMO with i.i.d. links, the traditional method of training each transmit antenna independently is DoF optimal, whereas we observe that for the asymmetric 2x2 MIMO, the traditional training is not gDoF-optimal. We extend this observation to a larger MxM MIMO by demonstrating a strategy that can achieve larger gDoF than a traditional training-based method.

Connected through hyperedge HE-1 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks"):
  with members:
    - "capacity approximations for gaussian relay networks"
    - "cooperative relaying at finite snr role of quantize map and forward"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "on noncoherent fading relay channels at high signal to noise ratio"
    - "wireless network simplification the gaussian n relay diamond network"
    - "approximately achieving gaussian relay network capacity with lattice codes"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"

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)