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: "phase diagram and approximate message passing for blind calibration and dictionary learning"
  Abstract: We consider dictionary learning and blind calibration for signals and matrices created from a random ensemble. We study the mean-squared error in the limit of large signal dimension using the replica method and unveil the appearance of phase transitions delimiting impossible, possible-but-hard and possible inference regions. We also introduce an approximate message passing algorithm that asymptotically matches the theoretical performance, and show through numerical tests that it performs very well, for the calibration problem, for tractable system sizes.

Connected through hyperedge HE-1 (induced by source paper "matrix calibration based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser mimo"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "passive beamforming and information transfer via large intelligent surface"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"

Connected through hyperedge HE-2 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "low rank modeling and its applications in image analysis"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-3 (induced by source paper "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"):
  with members:
    - "mixed adc massive mimo"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "one bit massive mimo channel estimation and high order modulations"
    - "limited feedback in multiple antenna systems with one bit quantization"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "joint channel and data estimation for large mimo systems with low precision adcs"

Connected through hyperedge HE-4 (induced by source paper "phase transitions and sample complexity in bayes optimal matrix factorization"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "information theoretically optimal sparse pca"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "new algorithms for learning incoherent and overcomplete dictionaries"

Connected through hyperedge HE-5 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "low complexity multiclass encryption by compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "joint channel and data estimation for large mimo systems with low precision adcs"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "performance limits of massive mimo systems based on bayes optimal inference"
    - "bilinear generalized approximate message passing part i derivation"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "massive mimo with 1 bit adc"
    - "distributed reception with spatial multiplexing mimo systems for the internet of things"

Connected through hyperedge HE-7 (induced by source paper "blind sensor calibration using approximate message passing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "on convergence of approximate message passing"
    - "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"
    - "blind calibration in compressed sensing using message passing algorithms"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Connected through hyperedge HE-8 (induced by source paper "performance limits of massive mimo systems based on bayes optimal inference"):
  with members:
    - "linear precoding for the mimo multiple access channel with finite alphabet inputs and statistical csi"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"
    - "compressed sensing under matrix uncertainty optimum thresholds and robust approximate message passing"
    - "on an achievable rate of large rayleigh block fading mimo channels with no csi"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "statistical mechanical analysis of the kronecker channel model for multiple input multiple output wireless communication"

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)