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: "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"
  Abstract: In continuation to a recent work on the statistical-mechanical analysis of minimum mean square error (MMSE) estimation in Gaussian noise via its relation to the mutual information (the I-MMSE relation), here we propose a simple and more direct relationship between optimum estimation and certain information measures (e.g., the information density and the Fisher information), which can be viewed as partition functions and hence are amenable to analysis using statistical-mechanical techniques. The proposed approach has several advantages, most notably, its applicability to general sources and channels, as opposed to the I-MMSE relation and its variants which hold only for certain classes of channels (e.g., additive white Gaussian noise channels). We then demonstrate the derivation of the conditional mean estimator and the MMSE in a few examples. Two of these examples turn out to be generalizable to a fairly wide class of sources and channels. For this class, the proposed approach is shown to yield an approximate conditional mean estimator and an MMSE formula that has the flavor of a single-letter expression. We also show how our approach can easily be generalized to situations of mismatched estimation.

Connected through hyperedge HE-1 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends"):
  with members:
    - "asymptotic performance analysis of a k hop amplify and forward relay mimo channel"
    - "iterative distributed minimum total mse approach for secure communications in mimo interference channels"
    - "asymptotic analysis of su mimo channels with transmitter noise and mismatched joint decoding"
    - "secure massive mimo transmission with an active eavesdropper"
    - "interference aligned space time transmission with diversity for the 2 times 2 x network"
    - "space time encoded miso broadcast channel with outdated csit an error rate and diversity performance analysis"
    - "high snr asymptotics of mutual information for discrete constellations with applications to bicm"
    - "differential distributed space time coding with imperfect synchronization in frequency selective channels"

Connected through hyperedge HE-2 (induced by source paper "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"):
  with members:
    - "asymptotic mmse analysis under sparse representation modeling"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "information theoretic proofs of entropy power inequalities"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-3 (induced by source paper "on compressive sensing in coding problems a rigorous approach"):
  with members:
    - "optimal phase transitions in compressed sensing"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "asymptotic mmse analysis under sparse representation modeling"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "performance regions in compressed sensing from noisy measurements"
    - "support recovery with sparsely sampled free random matrices"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "a deterministic equivalent for the analysis of non gaussian correlated mimo multiple access channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "analysis of mismatched estimation errors using gradients of partition functions"):
  with members:
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "on regret of parametric mismatch in minimum mean square error estimation"
    - "mutual information relative entropy and estimation in the poisson channel"
    - "message passing algorithms for compressed sensing"
    - "universal minimax discrete denoising under channel uncertainty"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "mutual information relative entropy and estimation in the poisson channel"):
  with members:
    - "the generalized area theorem and some of its consequences"
    - "randomly spread cdma asymptotics via statistical physics"
    - "life above threshold from list decoding to area theorem and mse"
    - "mutual information and minimum mean square error in gaussian channels"

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)