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: "multiple access gaussian channels with arbitrary inputs optimal precoding and power allocation"
  Abstract: In this paper, we derive new closed-form expressions for the gradient of the mutual information with respect to arbitrary parameters of the two-user multiple access channel (MAC). The derived relations generalize the fundamental relation between the derivative of the mutual information and the minimum mean squared error (MMSE) to multiuser setups. We prove that the derivative of the mutual information with respect to the signal to noise ratio (SNR) is equal to the MMSE plus a covariance induced due to the interference, quantified by a term with respect to the cross correlation of the multiuser input estimates, the channels and the precoding matrices. We also derive new relations for the gradient of the conditional and non-conditional mutual information with respect to the MMSE. Capitalizing on the new fundamental relations, we investigate the linear precoding and power allocation policies that maximize the mutual information for the two-user MAC Gaussian channels with arbitrary input distributions. We show that the optimal design of linear precoders may satisfy a fixed-point equation as a function of the channel and the input constellation under specific setups. We show also that the non-mutual interference in a multiuser setup introduces a term to the gradient of the mutual information which plays a fundamental role in the design of optimal transmission strategies, particularly the optimal precoding and power allocation, and explains the losses in the data rates. Therefore, we provide a novel interpretation of the interference with respect to the channel, power, and input estimates of the main user and the interferer.

Connected through hyperedge HE-1 (induced by source paper "mimo mutli cell processing optimal precoding and power allocation"):
  with members:
    - "piggybacking codes for network coding the high low snr regime"
    - "multi cell processing with limited cooperation a novel framework to timely designs and reduced feedback with general inputs"
    - "fundamental limits of cooperation"
    - "on optimal precoding in linear vector gaussian channels with arbitrary input distribution"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "network coding connections between information theory and estimation theory"):
  with members:
    - "multiuser i mmse"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "generalized i mmse for k user gaussian channels"):
  with members:
    - "information estimation and lookahead in the gaussian channel"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "multiuser i mmse"):
  with members:
    - "multi cell processing with limited cooperation a novel framework to timely designs and reduced feedback with general inputs"
    - "communications inspired projection design with application to compressive sensing"
    - "communications inspired linear discriminant analysis"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "multi cell processing with limited cooperation a novel framework to timely designs and reduced feedback with general inputs"):
  with members:
    - "degrees of freedom of time correlated miso broadcast channel with delayed csit"
    - "fundamental limits of cooperation"
    - "completely stale transmitter channel state information is still very useful"
    - "on optimal precoding in linear vector gaussian channels with arbitrary input distribution"
    - "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)