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: "fountain code inspired channel estimation for multi user millimeter wave mimo systems"
  Abstract: This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/receive beamforming directions until the channel estimate converges. We formulate the channel estimation process as a compressed sensing problem and apply a sparse estimation approach to recover the virtual channel information. As SWIFT does not adapt the BS's transmitting beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform existing random-beamforming based approaches that use a fixed number of measurements, over a range of signal-to-noise ratios and channel coherence times.

Connected through hyperedge HE-1 (induced by source paper "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "massive mimo ten myths and one critical question"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-2 (induced by source paper "beam on graph simultaneous channel estimation in multi user millimeter wave mimo systems"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "massive mimo ten myths and one critical question"
    - "limited feedback hybrid precoding for multi user millimeter wave 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)