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: "soft input soft output single tree search sphere decoding"
  Abstract: Soft-input soft-output (SISO) detection algorithms form the basis for iterative decoding. The computational complexity of SISO detection often poses significant challenges for practical receiver implementations, in particular in the context of multiple-input multiple-output (MIMO) wireless communication systems. In this paper, we present a low-complexity SISO sphere-decoding algorithm, based on the single tree-search paradigm proposed originally for soft-output MIMO detection in Studer, et al., IEEE J-SAC, 2008. The new algorithm incorporates clipping of the extrinsic log-likelihood ratios (LLRs) into the tree-search, which results in significant complexity savings and allows to cover a large performance/complexity tradeoff region by adjusting a single parameter. Furthermore, we propose a new method for correcting approximate LLRs --resulting from sub-optimal detectors-- which (often significantly) improves detection performance at low additional computational complexity.

Connected through hyperedge HE-1 (induced by source paper "signal processing for high throughput satellites challenges in new interference limited scenarios"):
  with members:
    - "clustering strategies for multicast precoding in multi beam satellite systems"
    - "generalized multicast multibeam precoding for satellite communications"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "admm based fast algorithm for multi group multicast beamforming in large scale wireless systems"
    - "resource management in non orthogonal multiple access networks for 5g and beyond"
    - "massive mimo ten myths and one critical question"
    - "hybrid beamforming for massive mimo a survey"
    - "dvb s2x enabled precoding for high throughput satellite systems"

Connected through hyperedge HE-2 (induced by source paper "deep learning for joint mimo detection and channel decoding"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "deep learning based mimo communications"
    - "on deep learning based channel decoding"
    - "adam a method for stochastic optimization"
    - "asymptotic performance of linear receivers in mimo fading channels"

Connected through hyperedge HE-3 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-4 (induced by source paper "circular sphere decoding a low complexity detection for mimo systems with general two dimensional signal constellations"):
  with members:
    - "sparsity aware sphere decoding algorithms and complexity analysis"
    - "block orthogonal space time code structure and its impact on qrdm decoding complexity reduction"

Connected through hyperedge HE-5 (induced by source paper "quantized precoding for massive mu mimo"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "massive mu mimo ofdm downlink with one bit dacs and linear precoding"
    - "nonlinear 1 bit precoding for massive mu mimo with higher order modulation"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "a remark on channels with transceiver distortion"

Connected through hyperedge HE-6 (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-7 (induced by source paper "quantized massive mu mimo ofdm uplink"):
  with members:
    - "mixed adc massive mimo"
    - "one bit massive mimo channel estimation and high order modulations"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "a field guide to forward backward splitting with a fasta implementation"

Connected through hyperedge HE-8 (induced by source paper "large scale mimo detection for 3gpp lte algorithms and fpga implementations"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "massive mimo how many antennas do we need"
    - "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)