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: "grouping based interference alignment with ia cell assignment in multi cell mimo mac under limited feedback"
  Abstract: Interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance the capacity of a wireless communication network. This paper proposes a grouping-based interference alignment (GIA) with optimized IA-Cell assignment for the multiple cells interfering multiple-input and multiple-output (MIMO) multiple access channel (MAC) network under limited feedback. This work consists of three main parts: 1) a complete study (including some new improvements) of the GIA with respect to the degrees of freedom (DoF) and optimal linear transceiver design is performed, which allows for low-complexity and distributed implementation; 2) based on the GIA, the concept of IA-Cell assignment is introduced. Three IA-Cell assignment algorithms are proposed for the setup with different backhaul overhead and their DoF and rate performance is investigated; 3) the performance of the proposed GIA algorithms is studied under limited feedback of IA precoders. To enable efficient feedback, a dynamic feedback bit allocation (DBA) problem is formulated and solved in closed-form. The practical implementation, the required backhaul overhead, and the complexity of the proposed algorithms are analyzed. Numerical results show that our proposed algorithms greatly outperform the traditional GIA under both unlimited and limited feedback.

Connected through hyperedge HE-1 (induced by source paper "robust transceiver design based on interference alignment for multi user multi cell mimo networks with channel uncertainty"):
  with members:
    - "two stage beamformer design for massive mimo downlink by trace quotient formulation"
    - "exploiting known interference as green signal power for downlink beamforming optimization"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "rethinking the role of interference in wireless networks"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "multi cell random beamforming achievable rate and degrees of freedom region"
    - "outage probability and outage based robust beamforming for mimo interference channels with imperfect channel state information"

Connected through hyperedge HE-2 (induced by source paper "hybrid beamforming with spatial modulation in multi user massive mimo mmwave networks"):
  with members:
    - "hybrid beamforming for large antenna arrays with phase shifter selection"
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

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)