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: "efficient optimal joint channel estimation and data detection for massive mimo systems"
  Abstract: In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

Connected through hyperedge HE-1 (induced by source paper "efficient dsp and circuit architectures for massive mimo state of the art and future directions"):
  with members:
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "out of band radiation from antenna arrays clarified"
    - "spatial characteristics of distortion radiated from antenna arrays with transceiver nonlinearities"
    - "decentralized baseband processing for massive mu mimo systems"
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "on out of band emissions of quantized precoding in massive mu mimo ofdm"
    - "achievable uplink rates for massive mimo with coarse quantization"

Connected through hyperedge HE-2 (induced by source paper "vlsi designs for joint channel estimation and data detection in large simo wireless systems"):
  with members:
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "joint channel estimation data detection in mimo fbmc oqam systems a tensor based approach"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "high throughput data detection for massive mu mimo ofdm using coordinate descent"
    - "optimal non coherent data detection for massive simo wireless systems with general constellations a polynomial complexity solution"
    - "fifty years of mimo detection the road to large scale mimos"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"

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)