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: "upper and lower bounds on the capacity of amplitude constrained mimo channels"
  Abstract: In this work, novel upper and lower bounds for the capacity of channels with arbitrary constraints on the support of the channel input symbols are derived. As an immediate practical application, the case of multiple-input multiple-output channels with amplitude constraints is considered. The bounds are shown to be within a constant gap if the channel matrix is invertible and are tight in the high amplitude regime for arbitrary channel matrices. Moreover, in the high amplitude regime, it is shown that the capacity scales linearly with the minimum between the number of transmit and receive antennas, similarly to the case of average power-constrained inputs.

Connected through hyperedge HE-1 (induced by source paper "the capacity achieving distribution for the amplitude constrained additive gaussian channel an upper bound on the number of mass points"):
  with members:
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "on the capacity of vector gaussian channels with bounded inputs"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "on the capacity of mimo optical wireless channels"):
  with members:
    - "capacity results on multiple input single output wireless optical channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "on the capacity of free space optical intensity channels"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of the peak power constrained vector gaussian channel an estimation theoretic perspective"):
  with members:
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "on the capacity of vector gaussian channels with bounded inputs"
    - "peak to average power ratio of good codes for gaussian channel"
    - "pointwise relations between information and estimation in gaussian noise"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "on continuous time gaussian channels"):
  with members:
    - "recent results in continuous time network information theory"
    - "wireless network information flow a deterministic approach"
    - "limit theorems in hidden markov models"
    - "directed information causal estimation and communication in continuous time"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "on the gaussian mac with imperfect feedback"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

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)