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: "two way relaying under the presence of relay transceiver hardware impairments"
  Abstract: Hardware impairments in physical transceivers are known to have a deleterious effect on communication systems; however, very few contributions have investigated their impact on relaying. This paper quantifies the impact of transceiver impairments in a two-way amplify-and-forward configuration. More specifically, the effective signal-to-noise-and-distortion ratios at both transmitter nodes are obtained. These are used to deduce exact and asymptotic closed-form expressions for the outage probabilities (OPs), as well as tractable formulations for the symbol error rates (SERs). It is explicitly shown that non-zero lower bounds on the OP and SER exist in the high-power regime-this stands in contrast to the special case of ideal hardware, where the OP and SER go asymptotically to zero.

Connected through hyperedge HE-1 (induced by source paper "impact of residual transmit rf impairments on training based mimo systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the mimo capacity with residual transceiver hardware impairments"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on the impact of hardware impairments on massive mimo"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"
    - "a new look at dual hop relaying performance limits with hardware impairments"

Connected through hyperedge HE-2 (induced by source paper "cooperative relaying in underlay cognitive systems with hardware impairments"):
  with members:
    - "on the mimo capacity with residual transceiver hardware impairments"
    - "a new look at dual hop relaying performance limits with hardware impairments"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "a new look at dual hop relaying performance limits with hardware impairments"):
  with members:
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"
    - "optimal coordinated beamforming in the multicell downlink with transceiver impairments"
    - "mimo transmission with residual transmit rf impairments"

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)