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: "faster recursions in sphere decoding"
  Abstract: Most of the calculations in standard sphere decoders are redundant in the sense that they either calculate quantities that are never used or calculate some quantities more than once. A new method, which is applicable to lattices as well as finite constellations, is proposed to avoid these redundant calculations while still returning the same result. Pseudocode is given to facilitate immediate implementation. Simulations show that the speed gain with the proposed method increases linearly with the lattice dimension. At dimension 60, the new algorithms avoid about 75% of all floating-point operations.

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 "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution"):
  with members:
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "discrete gaussian sampling reduces to cvp and svp"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "massive mimo for next generation wireless systems"
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "modulus zero forcing detection for mimo channels"):
  with members:
    - "fifty years of mimo detection the road to large scale mimos"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "compute and forward harnessing interference through structured codes"
    - "communication over mimo broadcast channels using lattice basis reduction"
    - "lll reduction achieves the receive diversity in mimo decoding"

Connected through hyperedge HE-4 (induced by source paper "asymptotic analysis of random lattices in high dimensions"):
  with members:
    - "fifty years of mimo detection the road to large scale mimos"
    - "effects of the lll reduction on the success probability of the babai point and on the complexity of sphere decoding"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "asymptotic performance of linear receivers in mimo fading channels"
    - "on the limitations of the naive lattice decoding"

Connected through hyperedge HE-5 (induced by source paper "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "efficient soft input soft output tree detection via an improved path metric"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "soft input soft output single tree search sphere decoding"

Connected through hyperedge HE-6 (induced by source paper "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"):
  with members:
    - "efficient soft input soft output tree detection via an improved path metric"
    - "lattice sequential decoder for coded mimo channel performance and complexity analysis"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "lattice sequential decoding for last coded mimo channels achievable rate dmt and complexity analysis"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "soft input soft output single tree search sphere decoding"
    - "near optimal detection in mimo systems using gibbs sampling"

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)