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: "superposition coding is almost always optimal for the poisson broadcast channel"
  Abstract: This paper shows that the capacity region of the continuous-time Poisson broadcast channel is achieved via superposition coding for most channel parameter values. Interestingly, the channel in some subset of these parameter values does not belong to any of the existing classes of broadcast channels for which superposition coding is optimal (e.g., degraded, less noisy, more capable). In particular, we introduce the notion of effectively less noisy broadcast channel and show that it implies less noisy but is not in general implied by more capable. For the rest of the channel parameter values, we show that there is a gap between Marton's inner bound and the UV outer bound.

Connected through hyperedge HE-1 (induced by source paper "extracting robust and accurate features via a robust information bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "unifying the brascamp lieb inequality and the entropy power inequality"
    - "where is the information in a deep neural network"
    - "a family of bayesian cram er rao bounds and consequences for log concave priors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "does learning require memorization a short tale about a long tail"
    - "defending against whitebox adversarial attacks via randomized discretization"

Connected through hyperedge HE-2 (induced by source paper "unifying the brascamp lieb inequality and the entropy power inequality"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "yet another proof of the entropy power inequality"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "an extremal inequality for long markov chains"
    - "information theoretic proofs of entropy power inequalities"

Connected through hyperedge HE-3 (induced by source paper "on the achievable rate and capacity for a sample based practical photon counting receiver"):
  with members:
    - "clipping noise and power allocation for ofdm based optical wireless communication using photon detection"
    - "estimating mixture entropy with pairwise distances"
    - "a refined analysis of the poisson channel in the high photon efficiency regime"

Connected through hyperedge HE-4 (induced by source paper "analytical model of wireless cell with superposition coding"):
  with members:
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "uplink capacity and interference avoidance for two tier femtocell networks"

Connected through hyperedge HE-5 (induced by source paper "the stochastic calculus approach to multi receiver poisson channels"):
  with members:
    - "the degraded poisson wiretap 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)