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: "sparse channel estimation for massive mimo with 1 bit feedback per dimension"
  Abstract: In massive multiple-input multiple-output (MIMO) systems, acquisition of the channel state information at the transmitter side (CSIT) is crucial. In this paper, a practical CSIT estimation scheme is proposed for frequency division duplexing (FDD) massive MIMO systems. Specifically, each received pilot symbol is first quantized to one bit per dimension at the receiver side and then the quantized bits are fed back to the transmitter. A joint one-bit compressed sensing algorithm is implemented at the transmitter to recover the channel matrices. The algorithm leverages the hidden joint sparsity structure in the user channel matrices to minimize the training and feedback overhead, which is considered to be a major challenge for FDD systems. Moreover, the one-bit compressed sensing algorithm accurately recovers the channel directions for beamforming. The one-bit feedback mechanism can be implemented in practical systems using the uplink control channel. Simulation results show that the proposed scheme nearly achieves the maximum output signal-to-noise-ratio for beamforming based on the estimated CSIT.

Connected through hyperedge HE-1 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-2 (induced by source paper "limited feedback channel estimation in massive mimo with non uniform directional dictionaries"):
  with members:
    - "overview of full dimension mimo in lte advanced pro"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "preliminary results on 3d channel modeling from theory to standardization"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"):
  with members:
    - "compressed sensing for wireless communications useful tips and tricks"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "kronecker product correlation model and limited feedback codebook design in a 3d channel model"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna 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.NI(Networking and Internet Architecture)