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: "channel estimation for orthogonal time frequency space otfs massive mimo"
  Abstract: Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.

Connected through hyperedge HE-1 (induced by source paper "otfs based receiver scheme with multi antennas in high mobility v2x systems"):
  with members:
    - "low complexity linear equalization for otfs systems with rectangular waveforms"
    - "otfs noma an efficient approach for exploiting heterogenous user mobility profiles"
    - "robust beamforming design for otfs noma"
    - "mimo otfs in high doppler fading channels signal detection and channel estimation"
    - "interference cancellation and iterative detection for orthogonal time frequency space modulation"
    - "high mobility ofdm downlink transmission with large scale antenna array"
    - "analysis of discrete time mimo ofdm based orthogonal time frequency space modulation"

Connected through hyperedge HE-2 (induced by source paper "on the effectiveness of otfs for joint radar and communication"):
  with members:
    - "low complexity linear equalizers for otfs exploiting two dimensional fast fourier transform"
    - "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"
    - "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-3 (induced by source paper "low complexity linear equalization for otfs systems with rectangular waveforms"):
  with members:
    - "otfs noma an efficient approach for exploiting heterogenous user mobility profiles"
    - "otfs a new generation of modulation addressing the challenges of 5g"
    - "interference cancellation and iterative detection for orthogonal time frequency space modulation"
    - "a simple two stage equalizer with simplified orthogonal time frequency space modulation over rapidly time varying channels"
    - "analysis of discrete time mimo ofdm based orthogonal time frequency space modulation"
    - "low complexity modem structure for ofdm based orthogonal time frequency space modulation"

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)