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 near optimal signal detection for uplink large scale mimo systems"
  Abstract: Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof of the proposed algorithm. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

Connected through hyperedge HE-1 (induced by source paper "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"):
  with members:
    - "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy efficient sensing and communication of parallel gaussian sources"
    - "par aware large scale multi user mimo ofdm downlink"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "fixing convergence of gaussian belief propagation"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-2 (induced by source paper "near optimal hybrid analog and digital precoding for downlink mmwave massive mimo systems"):
  with members:
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "suboptimal spatial diversity scheme for 60 ghz millimeter wave wlan"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-3 (induced by source paper "low complexity soft output signal detection based on gauss seidel method for uplink multi user large scale mimo systems"):
  with members:
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"

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)