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: "the wideband slope of interference channels the large bandwidth case"
  Abstract: It is well known that minimum received energy per bit in the interference channel is -1.59dB as if there were no interference. Thus, the best way to mitigate interference is to operate the interference channel in the low-SNR regime. However, when the SNR is small but non-zero, minimum energy per bit alone does not characterize performance. Verdu introduced the wideband slope S_0 to characterize the performance in this regime. We show that a wideband slope of S_0/S_{0,no interference}=1/2 is achievable. This result is similar to recent results on degrees of freedom in the high SNR regime, and we use a type of interference alignment using delays to obtain the result. We also show that in many cases the wideband slope is upper bounded by S_0/S_{0,no interference}<=1/2 for large number of users K .

Connected through hyperedge HE-1 (induced by source paper "capacity bounds for the k user gaussian interference channel"):
  with members:
    - "on the high snr capacity of the gaussian interference channel and new capacity bounds"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "wireless network information flow a deterministic approach"
    - "settling the feasibility of interference alignment for the mimo interference channel the symmetric square case"
    - "interference alignment from degrees of freedom to constant gap capacity approximations"
    - "a new sum rate outer bound for interference channels with three source destination pairs"
    - "k user interference channels general outer bound and sum capacity for certain gaussian channels"

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)