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: "statistical mechanical analysis of the kronecker channel model for multiple input multiple output wireless communication"
  Abstract: The Kronecker channel model of wireless communication is analyzed using statistical mechanics methods. In the model, spatial proximities among transmission/reception antennas are taken into account as certain correlation matrices, which generally yield nontrivial dependence among symbols to be estimated. This prevents accurate assessment of the communication performance by naively using a previously developed analytical scheme based on a matrix integration formula. In order to resolve this difficulty, we develop a formalism that can formally handle the correlations in Kronecker models based on the known scheme. Unfortunately, direct application of the developed scheme is, in general, practically difficult. However, the formalism is still useful, indicating that the effect of the correlations generally increase after the fourth order with respect to correlation strength. Therefore, the known analytical scheme offers a good approximation in performance evaluation when the correlation strength is sufficiently small. For a class of specific correlation, we show that the performance analysis can be mapped to the problem of one-dimensional spin systems in random fields, which can be investigated without approximation by the belief propagation algorithm.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on sparse vector recovery performance in structurally orthogonal matrices via lasso"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "multi processor approximate message passing using lossy compression"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-5 (induced by source paper "signal recovery using expectation consistent approximation for linear observations"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "expectation propagation for approximate bayesian inference"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

Connected through hyperedge HE-6 (induced by source paper "analysis of compressed sensing with spatially coupled orthogonal matrices"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the effect of spatial coupling on compressive sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

Connected through hyperedge HE-7 (induced by source paper "a deterministic equivalent for the analysis of non gaussian correlated mimo multiple access channels"):
  with members:
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "on the precoder design of flat fading mimo systems equipped with mmse receivers a large system approach"
    - "multi cell mimo downlink with cell cooperation and fair scheduling a large system limit analysis"
    - "asymptotic moments for interference mitigation in correlated fading channels"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "optimal channel training in uplink network mimo systems"
    - "random matrix model for nakagami hoyt fading"
    - "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach"

Connected through hyperedge HE-8 (induced by source paper "statistical mechanical analysis of compressed sensing utilizing correlated compression matrix"):
  with members:
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

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)