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: "channel state feedback schemes for multiuser mimo ofdm downlink"
  Abstract: Channel state feedback schemes for the MIMO broadcast downlink have been widely studied in the frequency-flat case. This work focuses on the more relevant frequency selective case, where some important new aspects emerge. We consider a MIMO-OFDM broadcast channel and compare achievable ergodic rates under three channel state feedback schemes: analog feedback, direction quantized feedback and "time-domain" channel quantized feedback. The first two schemes are direct extensions of previously proposed schemes. The third scheme is novel, and it is directly inspired by rate-distortion theory of Gaussian correlated sources. For each scheme we derive the conditions under which the system achieves full multiplexing gain. The key difference with respect to the widely treated frequency-flat case is that in MIMO-OFDM the frequency-domain channel transfer function is a Gaussian correlated source. The new time-domain quantization scheme takes advantage of the channel frequency correlation structure and outperforms the other schemes. Furthermore, it is by far simpler to implement than complicated spherical vector quantization. In particular, we observe that no structured codebook design and vector quantization is actually needed for efficient channel state information feedback.

Connected through hyperedge HE-1 (induced by source paper "deep convolutional compression for massive mimo csi feedback"):
  with members:
    - "machine learning in the air"
    - "mimo channel information feedback using deep recurrent network"
    - "conditional probability models for deep image compression"
    - "deep learning for massive mimo csi feedback"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "deep residual learning for image recognition"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"):
  with members:
    - "limited feedback channel estimation in massive mimo with non uniform directional dictionaries"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "compressed channel feedback for correlated massive mimo systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"):
  with members:
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "cell free massive mimo uniformly great service for everyone"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "joint channel training and feedback for fdd massive mimo systems"):
  with members:
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "structured matching pursuit for reconstruction of dynamic sparse channels"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "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"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "dynamic compressive sensing of time varying signals via approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"):
  with members:
    - "multi cell mimo downlink with cell cooperation and fair scheduling a large system limit analysis"
    - "multiuser miso transmitter optimization for intercell interference mitigation"
    - "on the optimal number of cooperative base stations in network mimo systems"
    - "base station cooperation on the downlink large system analysis"
    - "channel state feedback over the mimo mac"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "mimo downlink scheduling with non perfect channel state knowledge"
    - "training and feedback optimization for multiuser mimo downlink"

Connected through hyperedge HE-6 (induced by source paper "joint scheduling and arq for mu mimo downlink in the presence of inter cell interference"):
  with members:
    - "mimo downlink scheduling with non perfect channel state knowledge"
    - "mimo broadcast channel optimization under general linear constraints"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-7 (induced by source paper "limited rate channel state feedback for multicarrier block fading channels"):
  with members:
    - "low density codes achieve the rate distortion bound"
    - "limited feedback beamforming over temporally correlated channels"

Connected through hyperedge HE-8 (induced by source paper "mimo downlink scheduling with non perfect channel state knowledge"):
  with members:
    - "channel estimation and linear precoding in multiuser multiple antenna tdd systems"
    - "mode switching for the multi antenna broadcast channel based on delay and channel quantization"
    - "pilot contamination and precoding in multi cell tdd systems"

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)