human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust lattice alignment for k user mimo interference channels with imperfect channel knowledge"):
  Abstract: In this paper, we consider a robust lattice alignment design for K-user quasi-static multiple-input multiple-output (MIMO) interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite signal-to-noise ratio (SNR) or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method compared with the time-division multiple-access (TDMA), two-stage maximum-likelihood (ML) decoding, generalized Han-Kobayashi (HK), distributive IA and conventional IA methods in the literature.

Includes members:
  Member "real interference alignment exploiting the potential of single antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi antenna coded content delivery with caching a low complexity solution")
  Member "interference alignment for the k user mimo interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achievable dof delay trade offs for the k user mimo interference channel with delayed csit")
  Member "the approximate capacity of the many to one and one to many gaussian interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "on feasibility of interference alignment in mimo interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint optimization of signal design and resource allocation in wireless d2d edge computing")
  Member "on the degrees of freedom of the compound mimo broadcast channels with finite states":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")
  Member "on feasibility of interference alignment in mimo interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint optimization of signal design and resource allocation in wireless d2d edge computing")
  Member "ergodic interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the interference channel revisited aligning interference by adjusting receive antenna separation")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private information retrieval over gaussian mac")

Question: Please tell me which class the source paper 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)