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: "on the noisy feedback capacity of gaussian broadcast channels"
  Abstract: It is well known that, in general, feedback may enlarge the capacity region of Gaussian broadcast channels. This has been demonstrated even when the feedback is noisy (or partial-but-perfect) and only from one of the receivers. The only case known where feedback has been shown not to enlarge the capacity region is when the channel is physically degraded (El Gamal 1978, 1981). In this paper, we show that for a class of two-user Gaussian broadcast channels (not necessarily physically degraded), passively feeding back the stronger user's signal over a link corrupted by Gaussian noise does not enlarge the capacity region if the variance of feedback noise is above a certain threshold.

Connected through hyperedge HE-1 (induced by source paper "concatenated coding using linear schemes for gaussian broadcast channels with noisy channel output feedback"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "mimo mac bc duality with linear feedback coding schemes"
    - "an achievable rate region for the broadcast channel with feedback"
    - "capacity of all nine models of channel output feedback for the two user interference channel"
    - "using channel output feedback to increase throughput in hybrid arq"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"

Connected through hyperedge HE-2 (induced by source paper "coding schemes with rate limited feedback that improve over the nofeedback capacity for a large class of broadcast channels"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "feedback and partial message side information on the semideterministic broadcast channel"
    - "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"
    - "an achievable rate region for the broadcast channel with feedback"
    - "short message noisy network coding with a decode forward option"
    - "degrees of freedom of time correlated miso broadcast channel with delayed csit"
    - "optimal use of current and outdated channel state information degrees of freedom of the miso bc with mixed csit"
    - "lqg control approach to gaussian broadcast channels with feedback"

Connected through hyperedge HE-3 (induced by source paper "mimo mac bc duality with linear feedback coding schemes"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "on the ozarow leung scheme for the gaussian broadcast channel with feedback"
    - "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"
    - "an achievable rate region for the broadcast channel with feedback"
    - "any positive feedback rate increases the capacity of strictly less noisy broadcast channels"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"

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)