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: "the diversity order of the semidefinite relaxation detector"
  Abstract: We consider the detection of binary (antipodal) signals transmitted in a spatially multiplexed fashion over a fading multiple-input multiple-output (MIMO) channel and where the detection is done by means of semidefinite relaxation (SDR). The SDR detector is an attractive alternative to maximum likelihood (ML) detection since the complexity is polynomial rather than exponential. Assuming that the channel matrix is drawn with i.i.d. real valued Gaussian entries, we study the receiver diversity and prove that the SDR detector achieves the maximum possible diversity. Thus, the error probability of the receiver tends to zero at the same rate as the optimal maximum likelihood (ML) receiver in the high signal to noise ratio (SNR) limit. This significantly strengthens previous performance guarantees available for the semidefinite relaxation detector. Additionally, it proves that full diversity detection is in certain scenarios also possible when using a non-combinatorial receiver structure.

Connected through hyperedge HE-1 (induced by source paper "learning to detect"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "learning approximate neural estimators for wireless channel state information"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "learning to search for mimo detection"):
  with members:
    - "learning to detect"
    - "multilevel mimo detection with deep learning"
    - "two stage lasso admm signal detection algorithm for large scale mimo"
    - "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"

Connected through hyperedge HE-3 (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"
    - "a near maximum likelihood decoding algorithm for mimo systems based on semi definite programming"

Connected through hyperedge HE-4 (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"
    - "a low complexity gaussian message passing iterative detector for massive mu mimo systems"
    - "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"

Connected through hyperedge HE-5 (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-6 (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"
    - "a near maximum likelihood decoding algorithm for mimo systems based on semi definite programming"

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)