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: "optimal detection in training assisted simo systems with phase noise impairments"
  Abstract: In this paper, the problem of optimal maximum likelihood detection in a single user single-input multiple-output (SIMO) channel with phase noise at the receiver is considered. The optimal detection rules under training are derived for two operation modes, namely when the phase increments are fully correlated among the M receiver antennas (synchronous operation) and when they are independent (non-synchronous operation). The phase noise increments are parameterized by a very general distribution, which includes the Wiener phase noise model as a special case. It is proven that phase noise creates a symbol-error-rate (SER) floor for both operation modes. In the synchronous operation this error floor is independent of M, while it goes to zero exponentially with M in the non-synchronous operation.

Connected through hyperedge HE-1 (induced by source paper "ml detection in phase noise impaired simo channels with uplink training"):
  with members:
    - "capacity of simo and miso phase noise channels with common separate oscillators"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "distributed massive mimo in cellular networks impact of imperfect hardware and number of oscillators"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "calculation of the performance of communication systems from measured oscillator phase noise"
    - "massive mimo for next generation wireless systems"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"

Connected through hyperedge HE-2 (induced by source paper "distributed massive mimo in cellular networks impact of imperfect hardware and number of oscillators"):
  with members:
    - "capacity of simo and miso phase noise channels with common separate oscillators"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the impact of hardware impairments on massive mimo"
    - "a general framework for transmission with transceiver distortion and some applications"

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)