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: "a near maximum likelihood decoding algorithm for mimo systems based on semi definite programming"
  Abstract: In Multi-Input Multi-Output (MIMO) systems, Maximum-Likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems.

Connected through hyperedge HE-1 (induced by source paper "on the equivalence of semidifinite relaxations for mimo detection with general constellations"):
  with members:
    - "optimal estimation of sensor biases for asynchronous multi sensor data fusion"
    - "fifty years of mimo detection the road to large scale mimos"
    - "the equivalence of semidefinite relaxation mimo detectors for higher order qam"
    - "the diversity order of the semidefinite relaxation detector"

Connected through hyperedge HE-2 (induced by source paper "fifty years of mimo detection the road to large scale mimos"):
  with members:
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "an esprit based approach for 2 d localization of incoherently distributed sources in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "approximate bayesian probabilistic data association aided iterative detection for mimo systems using arbitrary m ary modulation"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "stable camera motion estimation using convex programming"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "robust camera location estimation by convex programming"
    - "global registration of multiple point clouds using semidefinite programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "robust pca via outlier pursuit"
    - "on affine rigidity"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "constellation precoded multiple beamforming"):
  with members:
    - "augmented lattice reduction for mimo decoding"
    - "constellation precoded beamforming"
    - "diversity analysis of bit interleaved coded multiple beamforming"
    - "reduced complexity sphere decoding for square qam via a new lattice representation"

Connected through hyperedge HE-5 (induced by source paper "the equivalence of semidefinite relaxation mimo detectors for higher order qam"):
  with members:
    - "lll reduction achieves the receive diversity in mimo decoding"
    - "complex lattice reduction algorithm for low complexity mimo detection"
    - "the diversity order of the semidefinite relaxation detector"

Connected through hyperedge HE-6 (induced by source paper "reduced complexity demodulation and equalization scheme for differential impulse radio uwb systems with isi"):
  with members:
    - "matrix lifting semi definite programming for decoding in multiple antenna systems"

Connected through hyperedge HE-7 (induced by source paper "the diversity order of the semidefinite relaxation detector"):
  with members:
    - "lll reduction achieves the receive diversity in mimo decoding"

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)