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: "a lower bound on the differential entropy of log concave random vectors with applications"
  Abstract: We derive a lower bound on the differential entropy of a log-concave random variable X in terms of the p-th absolute moment of X. The new bound leads to a reverse entropy power inequality with an explicit constant, and to new bounds on the rate-distortion function and the channel capacity. Specifically, we study the rate-distortion function for log-concave sources and distortion measure #R##N#          #R##N#            #R##N#              #R##N#                d#R##N#                #R##N#                  (#R##N#                  x#R##N#                  ,#R##N#                  #R##N#                    x#R##N#                    ^#R##N#                  #R##N#                  )#R##N#                #R##N#                =#R##N#                #R##N#                  #R##N#                    |#R##N#                    x#R##N#                    −#R##N#                    #R##N#                      x#R##N#                      ^#R##N#                    #R##N#                    |#R##N#                  #R##N#                  r#R##N#                #R##N#              #R##N#            #R##N#          #R##N#        , with #R##N#          #R##N#            #R##N#              #R##N#                r#R##N#                ≥#R##N#                1#R##N#              #R##N#            #R##N#          #R##N#        , and we establish that the difference between the rate-distortion function and the Shannon lower bound is at most #R##N#          #R##N#            #R##N#              #R##N#                log#R##N#                (#R##N#                #R##N#                  #R##N#                    π#R##N#                    e#R##N#                  #R##N#                #R##N#                )#R##N#                ≈#R##N#                1#R##N#                .#R##N#                5#R##N#              #R##N#            #R##N#          #R##N#         bits, independently of r and the target distortion d. For mean-square error distortion, the difference is at most #R##N#          #R##N#            #R##N#              #R##N#                log#R##N#                (#R##N#                #R##N#                  #R##N#                    #R##N#                      π#R##N#                      e#R##N#                    #R##N#                    2#R##N#                  #R##N#                #R##N#                )#R##N#                ≈#R##N#                1#R##N#              #R##N#            #R##N#          #R##N#         bit, regardless of d. We also provide bounds on the capacity of memoryless additive noise channels when the noise is log-concave. We show that the difference between the capacity of such channels and the capacity of the Gaussian channel with the same noise power is at most #R##N#          #R##N#            #R##N#              #R##N#                log#R##N#                (#R##N#                #R##N#                  #R##N#                    #R##N#                      π#R##N#                      e#R##N#                    #R##N#                    2#R##N#                  #R##N#                #R##N#                )#R##N#                ≈#R##N#                1#R##N#              #R##N#            #R##N#          #R##N#         bit. Our results generalize to the case of a random vector X with possibly dependent coordinates. Our proof technique leverages tools from convex geometry.

Connected through hyperedge HE-1 (induced by source paper "an integral representation of the logarithmic function with applications in information theory"):
  with members:
    - "stochastic ordering of fading channels through the shannon transform"

Connected through hyperedge HE-2 (induced by source paper "on the entropy power inequality for the r enyi entropy of order 0 1"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "wasserstein stability of the entropy power inequality for log concave densities"
    - "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"

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)