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: "ammse optimization for multiuser miso systems with imperfect csit and perfect csir"
  Abstract: In this paper, we consider the design of robust linear precoders for MU-MISO systems where users have perfect Channel State Information (CSI) while the BS has partial CSI. In particular, the BS has access to imperfect estimates of the channel vectors, in addition to the covariance matrices of the estimation error vectors. A closed-form expression for the Average Minimum Mean Square Error (AMMSE) is obtained using the second order Taylor Expansion. This approximation is used to formulate two fairness-based robust design problems: a maximum AMMSE-constrained problem and a power-constrained problem. We propose an algorithm based on convex optimization techniques to address the first problem, while the second problem is tackled by exploiting the close relationship between the two problems, in addition to their monotonie natures.

Connected through hyperedge HE-1 (induced by source paper "qos constrained power minimization in the miso broadcast channel with imperfect csi"):
  with members:
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-2 (induced by source paper "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"):
  with members:
    - "miso networks with imperfect csit a topological rate splitting approach"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "a rate splitting strategy for massive mimo with imperfect csit"
    - "sum rate maximization for mu miso with partial csit using joint multicasting and broadcasting"
    - "rate analysis of two receiver miso broadcast channel with finite rate feedback a rate splitting approach"
    - "achieving max min fairness for mu miso with partial csit a multicast assisted transmission"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "transmit designs for the mimo broadcast channel with statistical csi"

Connected through hyperedge HE-3 (induced by source paper "qos constrained power minimization in the multiple stream mimo broadcast channel"):
  with members:
    - "qos constrained power minimization in the miso broadcast channel with imperfect csi"
    - "a general rate duality of the mimo multiple access channel and the mimo broadcast channel"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-4 (induced by source paper "achieving max min fairness for mu miso with partial csit a multicast assisted transmission"):
  with members:
    - "miso broadcast channel with imperfect and un matched csit in the frequency domain dof region and transmission strategies"
    - "linear transceiver design for a mimo interfering broadcast channel achieving max min fairness"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

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)