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: "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"
  Abstract: In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle-information. To tackle these challenges, we propose a channel sparsity representation with a super-resolution sampling grid and a hidden Markovian support. Based on that, we develop a novel variational inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle-information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method.

Connected through hyperedge HE-1 (induced by source paper "double sparsity learning based channel and signal estimation in massive mimo with generalized spatial modulation"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "generalized spatial modulation in large scale multiuser mimo systems"
    - "bilinear generalized approximate message passing part i derivation"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-2 (induced by source paper "cascaded channel estimation for large intelligent metasurface assisted massive mimo"):
  with members:
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "bilinear generalized approximate message passing part i derivation"

Connected through hyperedge HE-3 (induced by source paper "semi blind channel and signal estimation for uplink massive mimo with channel sparsity"):
  with members:
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "bilinear generalized approximate message passing part i derivation"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"
    - "expectation maximization gaussian mixture approximate message passing"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "message passing algorithms for compressed sensing"

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)