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: "low complexity linear equalizers for otfs exploiting two dimensional fast fourier transform"
  Abstract: Orthogonal time frequency space (OTFS) modulation can effectively convert a doubly dispersive channel into an almost non-fading channel in the delay-Doppler domain. However, one critical issue for OTFS is the very high complexity of equalizers. In this letter, we first reveal the doubly block circulant feature of OTFS channel represented in the delay-Doppler domain. By exploiting this unique feature, we further propose zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers that can be efficiently implemented with the two-dimensional fast Fourier transform. The complexity of our proposed equalizers is gracefully reduced from $\mathcal{O}\left(\left(NM\right)^{3}\right)$ to $\mathcal{O}\left(NM\mathrm{log_{2}}\left(NM\right)\right)$, where $N$ and $M$ are the number of OTFS symbols and subcarriers, respectively. Analysis and simulation results show that compared with other existing linear equalizers for OTFS, our proposed linear equalizers enjoy a much lower computational complexity without any performance loss.

Connected through hyperedge HE-1 (induced by source paper "on the effectiveness of otfs for joint radar and communication"):
  with members:
    - "performance analysis of joint radar and communication using ofdm and otfs"
    - "joint radar communications strategies for autonomous vehicles"
    - "orthogonal time frequency space otfs modulation based radar system"
    - "channel estimation for orthogonal time frequency space otfs massive mimo"
    - "joint state sensing and communication optimal tradeoff for a memoryless case"
    - "embedded pilot aided channel estimation for otfs in delay doppler channels"
    - "interference cancellation and iterative detection for orthogonal time frequency space modulation"
    - "tracking of wideband multipath components in a vehicular communication scenario"

Connected through hyperedge HE-2 (induced by source paper "robust beamforming design for otfs noma"):
  with members:
    - "otfs noma an efficient approach for exploiting heterogenous user mobility profiles"
    - "otfs a new generation of modulation addressing the challenges of 5g"
    - "on otfs modulation for high doppler fading channels"
    - "interference cancellation and iterative detection for orthogonal time frequency space modulation"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

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)