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 region of gaussian random variables"
  Abstract: Given n (discrete or continuous) random variables X_i, the#R##N#(2^n-1)-dimensional vector obtained by evaluating the joint entropy of all non-empty subsets of {X_1,...,X_n} is called an entropic vector. Determining the region of entropic vectors is an important open problem with many#R##N#applications in information theory. Recently, it has been shown that the entropy regions for discrete and continuous random variables, though different, can be determined from one another. An important class of continuous random#R##N#variables are those that are vector-valued and jointly Gaussian. In this paper we give a full characterization of the convex cone of the entropy region of#R##N#three jointly Gaussian vector-valued random variables and prove that it is the same as the convex cone of three scalar-valued Gaussian random variables and#R##N#further that it yields the entire entropy region of 3 arbitrary random variables. We further determine the actual entropy region of 3 vector-valued jointly Gaussian random variables through a conjecture. For n≥4 number of#R##N#random variables, we point out a set of 2^n-1-(n(n+1))/2 minimal necessary and sufficient conditions that 2^n-1 numbers must satisfy in order to correspond to#R##N#the entropy vector of n scalar jointly Gaussian random variables. This improves on a result of Holtz and Sturmfels which gave a nonminimal set of conditions.#R##N#These constraints are related to Cayley's hyperdeterminant and hence with an eye towards characterizing the entropy region of jointly Gaussian random#R##N#variables, we also present some new results in this area. We obtain a new (determinant) formula for the 2x2x2 hyperdeterminant and we also give a new (transparent) proof of the fact that the principal minors of an n x n symmetric#R##N#matrix satisfy the 2 x 2 x ... x 2 (up to n times) hyperdeterminant relations.

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)