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: "the entropy power inequality and mrs gerber s lemma for abelian groups of order 2 n"
  Abstract: Shannon's Entropy Power Inequality can be viewed as characterizing the minimum differential entropy achievable by the sum of two independent random variables with fixed differential entropies. The entropy power inequality has played a key role in resolving a number of problems in information theory. It is therefore interesting to examine the existence of a similar inequality for discrete random variables. In this paper we obtain an entropy power inequality for random variables taking values in an abelian group of order 2^n, i.e. for such a group G we explicitly characterize the function f_G(x,y) giving the minimum entropy of the sum of two independent G-valued random variables with respective entropies x and y. Random variables achieving the extremum in this inequality are thus the analogs of Gaussians in this case, and these are also determined. It turns out that f_G(x,y) is convex in x for fixed y and, by symmetry, convex in y for fixed x. This is a generalization to abelian groups of order 2^n of the result known as Mrs. Gerber's Lemma.

Connected through hyperedge HE-1 (induced by source paper "a new entropy power inequality for integer valued random variables"):
  with members:
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "adaptive sensing using deterministic partial hadamard matrices"
    - "information theoretic proofs of entropy power inequalities"
    - "entropy power inequality for a family of discrete random variables"
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "generalized entropy power inequalities and monotonicity properties of information"
    - "an extremal inequality motivated by multiterminal information theoretic problems"

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)