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: "two stage lasso admm signal detection algorithm for large scale mimo"
  Abstract: This paper explores the benefit of using some of the machine learning techniques and Big data optimization tools in approximating maximum likelihood (ML) detection of Large Scale MIMO systems. First, large scale MIMO detection problem is formulated as a LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem. Then, Alternating Direction Method of Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is motivated by its ability of solving convex optimization problems by breaking them into smaller sub-problems, each of which are then easier to handle. Further improvement is obtained using two stages of LASSO with interference cancellation from the first stage. The proposed algorithm is investigated at various modulation techniques with different number of antennas. It is also compared with widely used algorithms in this field. Simulation results demonstrate the efficacy of the proposed algorithm for both uncoded and coded cases.

Connected through hyperedge HE-1 (induced by source paper "learning to search for mimo detection"):
  with members:
    - "learning to detect"
    - "multilevel mimo detection with deep learning"
    - "a model driven deep learning network for mimo detection"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to optimize"
    - "optimality of large mimo detection via approximate message passing"
    - "fifty years of mimo detection the road to large scale mimos"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a proximal jacobian admm approach for fast massive mimo signal detection in low latency communications"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a low complexity detection algorithm based on alternating minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-3 (induced by source paper "a low complexity detection algorithm based on alternating minimization"):
  with members:
    - "matrix inversion less signal detection using sor method for uplink large scale mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

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)