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: "fundamental limits in correlated fading mimo broadcast channels benefits of transmit correlation diversity"
  Abstract: We investigate asymptotic capacity limits of the Gaussian MIMO broadcast channel (BC) with spatially correlated fading to understand when and how much transmit correlation helps the capacity. By imposing a structure on channel covariances (equivalently, transmit correlations at the transmitter side) of users, also referred to as \emph{transmit correlation diversity}, the impact of transmit correlation on the power gain of MIMO BCs is characterized in several regimes of system parameters, with a particular interest in the large-scale array (or massive MIMO) regime. Taking the cost for downlink training into account, we provide asymptotic capacity bounds of multiuser MIMO downlink systems to see how transmit correlation diversity affects the system multiplexing gain. We make use of the notion of joint spatial division and multiplexing (JSDM) to derive the capacity bounds. It is advocated in this paper that transmit correlation diversity may be of use to significantly increase multiplexing gain as well as power gain in multiuser MIMO systems. In particular, the new type of diversity in wireless communications is shown to improve the system multiplexing gain up to by a factor of the number of degrees of such diversity. Finally, performance limits of conventional large-scale MIMO systems not exploiting transmit correlation are also characterized.

Connected through hyperedge HE-1 (induced by source paper "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"):
  with members:
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "trellis extended codebooks and successive phase adjustment a path from lte advanced to fdd massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"):
  with members:
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "compressed channel feedback for correlated massive mimo systems"
    - "channel vector subspace estimation from low dimensional projections"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-3 (induced by source paper "achievable rates of fdd massive mimo systems with spatial channel correlation"):
  with members:
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "blind pilot decontamination"
    - "aligned image sets under channel uncertainty settling a conjecture by lapidoth shamai and wigger on the collapse of degrees of freedom under finite precision csit"
    - "trellis extended codebooks and successive phase adjustment a path from lte advanced to fdd massive mimo systems"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "joint spatial division and multiplexing opportunistic beamforming and user grouping"

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)