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: "on conditions for linearity of optimal estimation"
  Abstract: When is optimal estimation linear? It is well known that, when a Gaussian source is contaminated with Gaussian noise, a linear estimator minimizes the mean square estimation error. This paper analyzes, more generally, the conditions for linearity of optimal estimators. Given a noise (or source) distribution, and a specified signal to noise ratio (SNR), we derive conditions for existence and uniqueness of a source (or noise) distribution for which the $L_p$ optimal estimator is linear. We then show that, if the noise and source variances are equal, then the matching source must be distributed identically to the noise. Moreover, we prove that the Gaussian source-channel pair is unique in the sense that it is the only source-channel pair for which the mean square error (MSE) optimal estimator is linear at more than one SNR values. Further, we show the asymptotic linearity of MSE optimal estimators for low SNR if the channel is Gaussian regardless of the source and, vice versa, for high SNR if the source is Gaussian regardless of the channel. The extension to the vector case is also considered where besides the conditions inherited from the scalar case, additional constraints must be satisfied to ensure linearity of the optimal estimator.

Connected through hyperedge HE-1 (induced by source paper "on the minimum mean p th error in gaussian noise channels and its applications"):
  with members:
    - "on communication through a gaussian channel with an mmse disturbance constraint"
    - "lower bounds on parameter modulation estimation under bandwidth constraints"
    - "interference as noise friend or foe"
    - "exponential error bounds on parameter modulation estimation for discrete memoryless channels"
    - "mmse of bad codes"
    - "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"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "on the equivalence of maximum snr and mmse estimation applications to additive non gaussian channels and quantized observations"):
  with members:
    - "bayesian estimation of a gaussian source in middleton s class a impulsive noise"
    - "pointwise relations between information and estimation in gaussian noise"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "zero delay joint source channel coding in the presence of interference known at the encoder"):
  with members:
    - "on zero delay source channel coding"
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "joint source channel codes for mimo block fading channels"

Connected through hyperedge HE-4 (induced by source paper "on zero delay source channel coding"):
  with members:
    - "a deterministic annealing approach to witsenhausen s counterexample"
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "a deterministic annealing approach to optimization of zero delay source channel codes"
    - "reduced dimension linear transform coding of correlated signals in networks"
    - "data processing bounds for scalar lossy source codes with side information at the decoder"

Connected through hyperedge HE-5 (induced by source paper "incremental refinement using a gaussian test channel"):
  with members:
    - "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)