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: "optimized training and feedback for mimo downlink channels"
  Abstract: We consider a MIMO fading broadcast channel where channel state information is acquired at user terminals via downlink training and channel feedback is used to provide transmitter channel state information (CSIT) to the base station. The feedback channel (the corresponding uplink) is modeled as an AWGN channel, orthogonal across users. The total bandwidth consumed is the sum of the bandwidth/resources used for downlink training, channel feedback, and data transmission. Assuming that the channel follows a block fading model and that zero-forcing beamforming is used, we optimize the net achievable rate for unquantized (analog) and quantized (digital) channel feedback. The optimal number of downlink training pilots is seen to be essentially the same for both feedback techniques, but digital feedback is shown to provide a larger net rate than analog feedback.

Connected through hyperedge HE-1 (induced by source paper "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"):
  with members:
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "massive mimo how many antennas do we need"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "channel estimation and linear precoding in multiuser multiple antenna tdd systems"
    - "base station cooperation on the downlink large system analysis"
    - "what is the value of joint processing of pilots and data in block fading channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-2 (induced by source paper "multiuser mimo achievable rates with downlink training and channel state feedback"):
  with members:
    - "channel state feedback over the mimo mac"
    - "mode switching for the multi antenna broadcast channel based on delay and channel quantization"
    - "channel state feedback schemes for multiuser mimo ofdm downlink"
    - "antenna combining for the mimo downlink channel"
    - "space division multiple access with a sum feedback rate constraint"
    - "multi user diversity vs accurate channel feedback for mimo broadcast channels"
    - "mimo broadcast channels with finite rate feedback"
    - "on the capacity of fading mimo broadcast channels with imperfect transmitter side information"

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)