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: "unifying the brascamp lieb inequality and the entropy power inequality"
  Abstract: The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) can be viewed as information inequalities concerning entropies of linear transformations of random variables. The EPI provides lower bounds for the entropy of linear transformations of random vectors with independent components. The BLI, on the other hand, provides upper bounds on the entropy of a random vector in terms of the entropies of its linear transformations. In this paper, we present a new entropy inequality that generalizes both the BLI and EPI by considering a variety of independence relations among the components of a random vector. Our main technical contribution is in the proof strategy that leverages the "doubling trick" to prove Gaussian optimality for certain entropy expressions under independence constraints.

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"
    - "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"
    - "a spectral view of adversarially robust features"

Connected through hyperedge HE-2 (induced by source paper "transportation proof of an inequality by anantharam jog and nair"):
  with members:
    - "equality in the matrix entropy power inequality and blind separation of real and complex sources"
    - "sum rate capacity for symmetric gaussian multiple access channels with feedback"
    - "yet another proof of the entropy power inequality"

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)