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 entropy power inequality for the r enyi entropy of order 0 1"
  Abstract: Using a sharp version of the reverse Young inequality, and a R\'enyi entropy comparison result due to Fradelizi, Madiman, and Wang, the authors are able to derive R\'enyi entropy power inequalities for log-concave random vectors when R\'enyi parameters belong to $(0,1)$. Furthermore, the estimates are shown to be sharp up to absolute constants.

Connected through hyperedge HE-1 (induced by source paper "sharp moment entropy inequalities and capacity bounds for log concave distributions"):
  with members:
    - "two remarks on generalized entropy power inequalities"
    - "bounds on variance for unimodal distributions"
    - "forward and reverse entropy power inequalities in convex geometry"
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "minimization problems based on relative alpha entropy i forward projection"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"

Connected through hyperedge HE-2 (induced by source paper "variants of the entropy power inequality"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "on renyi entropy power inequalities"
    - "strengthening the entropy power inequality"
    - "beyond the entropy power inequality via rearrangements"
    - "the concavity of renyi entropy power"
    - "an information theoretic proof of nash s inequality"
    - "information theoretic proofs of entropy power inequalities"
    - "generalized entropy power inequalities and monotonicity properties of information"

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)