human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a lower bound on the differential entropy of log concave random vectors with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an integral representation of the logarithmic function with applications in information theory")
  Member "forward and reverse entropy power inequalities in convex geometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transportation proofs of r enyi entropy power inequalities")
  Member "wasserstein stability of the entropy power inequality for log concave densities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a lower bound on the differential entropy of log concave random vectors with applications")
  Member "on renyi entropy power inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the renyi capacity and center")
  Member "variants of the entropy power inequality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transportation proofs of r enyi entropy power inequalities")
  Member "a concavity property for the reciprocal of fisher information and its consequences on costa s epi":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a lower bound on the differential entropy of log concave random vectors with applications")
  Member "a strengthened entropy power inequality for log concave densities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a lower bound on the differential entropy of log concave random vectors with applications")
  Member "the entropy per coordinate of a random vector is highly constrained under convexity conditions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constrained functional value under general convexity conditions with applications to distributed simulation")

Question: Please tell me which class the source paper 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)