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: "rate distortion function via minimum mean square error estimation"
  Abstract: We derive a simple general parametric representation of the rate-distortion function of a memoryless source, where both the rate and the distortion are given by integrals whose integrands include the minimum mean square error (MMSE) of the distortion $\Delta=d(X,Y)$ based on the source symbol $X$, with respect to a certain joint distribution of these two random variables. At first glance, these relations may seem somewhat similar to the I-MMSE relations due to Guo, Shamai and Verd\'u, but they are, in fact, quite different. The new relations among rate, distortion, and MMSE are discussed from several aspects, and more importantly, it is demonstrated that they can sometimes be rather useful for obtaining non-trivial upper and lower bounds on the rate-distortion function, as well as for determining the exact asymptotic behavior for very low and for very large distortion. Analogous MMSE relations hold for channel capacity as well.

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    - "space time encoded miso broadcast channel with outdated csit an error rate and diversity performance analysis"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 "another look at expurgated bounds and their statistical mechanical interpretation"):
  with members:
    - "the generalized random energy model and its application to the statistical physics of ensembles of hierarchical codes"
    - "error exponents for broadcast channels with degraded message sets"
    - "error exponents of optimum decoding for the interference channel"
    - "error exponents of erasure list decoding revisited via moments of distance enumerators"
    - "on the statistical physics of directed polymers in a random medium and their relation to tree codes"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-3 (induced by source paper "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"):
  with members:
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "threshold effects in parameter estimation as phase transitions in statistical mechanics"
    - "how could the replica method improve accuracy of performance assessment of channel coding"
    - "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)