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 low complexity gaussian message passing iterative detector for massive mu mimo systems"
  Abstract: This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) method over a pairwise graph for a massive Multiuser Multiple-Input Multiple-Output (MU-MIMO) system, in which a base station with M antennas serves K Gaussian sources simultaneously. Both K and M are large numbers and we consider the cases that K   (√2 − l)2. Therefore, a new convergent GMPID with equally low complexity called SA-GMPID is proposed, which converges to the MMSE detection for any K < M with a faster convergence speed. Finally, numerical results are provided to verify the validity and accuracy of the proposed theoretical results.

Connected through hyperedge HE-1 (induced by source paper "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"):
  with members:
    - "optimality of large mimo detection via approximate message passing"
    - "optimal non coherent data detection for massive simo wireless systems with general constellations a polynomial complexity solution"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-2 (induced by source paper "capacity achieving iterative lmmse detection for mimo noma systems"):
  with members:
    - "the application of mimo to non orthogonal multiple access"
    - "uplink non orthogonal multiple access for 5g wireless networks"
    - "a semiblind two way training method for discriminatory channel estimation in mimo systems"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "achievable rates of mimo systems with linear precoding and iterative lmmse detection"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-3 (induced by source paper "scalable uplink signal detection in c rans via randomized gaussian message passing"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "distributed clock skew and offset estimation in wireless sensor networks asynchronous algorithm and convergence analysis"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "fundamental limits of cooperation"
    - "generalized approximate message passing for estimation with random linear mixing"

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)