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: "wasserstein stability of the entropy power inequality for log concave densities"
  Abstract: We establish quantitative stability results for the entropy power inequality (EPI). Specifically, we show that if uniformly log-concave densities nearly saturate the EPI, then they must be close to Gaussian densities in the quadratic Wasserstein distance. Further, if one of the densities is log-concave and the other is Gaussian, then the deficit in the EPI can be controlled in terms of the $L^1$-Wasserstein distance. As a counterpoint, an example shows that the EPI can be unstable with respect to the quadratic Wasserstein distance when densities are uniformly log-concave on sets of measure arbitrarily close to one. Our stability results can be extended to non-log-concave densities, provided certain regularity conditions are met. The proofs are based on optimal transportation.

Connected through hyperedge HE-1 (induced by source paper "a lower bound on the differential entropy of log concave random vectors with applications"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "the shannon lower bound is asymptotically tight"
    - "links between the logarithmic sobolev inequality and the convolution inequalities for entropy and fisher information"
    - "a concavity property for the reciprocal of fisher information and its consequences on costa s epi"
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "data compression with low distortion and finite blocklength"
    - "a strengthened entropy power inequality for log concave densities"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"

Connected through hyperedge HE-2 (induced by source paper "the deficit in an entropic inequality"):
  with members:
    - "a strengthened entropy power inequality for log concave densities"

Connected through hyperedge HE-3 (induced by source paper "forward and reverse entropy power inequalities in convex geometry"):
  with members:
    - "on renyi entropy power inequalities"
    - "strengthening the entropy power inequality"
    - "variants of the entropy power inequality"
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "entropies of weighted sums in cyclic groups and an application to polar codes"
    - "beyond the entropy power inequality via rearrangements"
    - "a strengthened entropy power inequality for log concave densities"
    - "the concavity of renyi entropy power"

Connected through hyperedge HE-4 (induced by source paper "on the entropy power inequality for the r enyi entropy of order 0 1"):
  with members:
    - "a lower bound on the differential entropy of log concave random vectors with applications"
    - "forward and reverse entropy power inequalities in convex geometry"
    - "on renyi entropy power inequalities"
    - "variants of the entropy power inequality"
    - "a concavity property for the reciprocal of fisher information and its consequences on costa s epi"
    - "a strengthened entropy power inequality for log concave densities"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"
    - "generalized entropy power inequalities and monotonicity properties of information"

Connected through hyperedge HE-5 (induced by source paper "comment on the equality condition for the i mmse proof of entropy power inequality"):
  with members:
    - "optimal transportation to the entropy power inequality"
    - "yet another proof of the entropy power inequality"
    - "information theoretic proofs of entropy power inequalities"
    - "generalized entropy power inequalities and monotonicity properties of information"
    - "mutual information and minimum mean square error in gaussian channels"

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)