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: "on the degrees of freedom of the k user distributed broadcast channel"
  Abstract: In this paper, we consider the network MIMO channel under the so-called distributed Channel State Information (CSI) configuration. In that setting, each Transmitter (TX) shares the common knowledge of all user's data symbols but has only its own imperfect estimate of the CSI, based on which it designs its transmission coefficients in a decentralized manner. This setting could for example be encountered when the user's data symbols are cached beforehand at the TXs or in a Cloud-RAN scenario with partial centralization where the CSI sharing between TXs suffers from the delay due to the sharing through the backhaul network. Our first contribution consists in rigorously computing an upper-bound for the DoF through the analysis of a genie-aided setting. Building on this upper-bound, we present a new robust decentralized transmission scheme that achieves the optimal DoF over several important CSI configurations. In strong contrast to the conventional zero-forcing scheme which is known to be limited by the worst estimate across TXs, the proposed scheme is able to leverage the more accurate CSI at few TXs to improve the DoF. Surprisingly, we show that there is a CSI regime in which the optimal DoF only depends on the CSI quality at the best TX, such that sharing the channel estimate to the other TXs does not improve the DoF.

Connected through hyperedge HE-1 (induced by source paper "dof region of the decentralized mimo broadcast channel unicode x2014 how many informed antennas do we need"):
  with members:
    - "sum set inequalities from aligned image sets instruments for robust gdof bounds"
    - "achievable dof regions of mimo networks with imperfect csit"
    - "degrees of freedom of the two user mimo broadcast channel with private and common messages under hybrid csit models"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "sum capacity of interference channels with a local view impact of distributed decisions"
    - "completely stale transmitter channel state information is still very useful"
    - "the two user gaussian interference channel a deterministic view"

Connected through hyperedge HE-2 (induced by source paper "achieving vanishing rate loss in decentralized network mimo"):
  with members:
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "fundamental limits of caching"
    - "mimo broadcast channels with finite rate 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)