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: "finding the maximizers of the information divergence from an exponential family"
  Abstract: This paper investigates maximizers of the information divergence from an exponential family e. It is shown that the rI -projection of a maximizer P to e is a convex combination of P and a probability measure P- with disjoint support and the same value of the sufficient statistics A. This observation can be used to transform the original problem of maximizing D(·∥e) over the set of all probability measures into the maximization of a function Dr over a convex subset of ker A. The global maximizers of both problems correspond to each other. Furthermore, finding all local maximizers of Dr yields all local maximizers of D(·∥E). This paper also proposes two algorithms to find the maximizers of Dr and applies them to two examples, where the maximizers of D(·∥e) were not known before.

Connected through hyperedge HE-1 (induced by source paper "stochastic feedforward neural networks universal approximation"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "simplified stochastic feedforward neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-2 (induced by source paper "factorized mutual information maximization"):
  with members:
    - "large scale study of curiosity driven learning"
    - "deep variational information bottleneck"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "a theory of cheap control in embodied systems"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "multivariate information bottleneck"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-3 (induced by source paper "restricted boltzmann machines introduction and review"):
  with members:
    - "geometry and expressive power of conditional restricted boltzmann machines"
    - "revisiting natural gradient for deep networks"
    - "information theoretic inference of common ancestors"

Connected through hyperedge HE-4 (induced by source paper "on the entropy of couplings"):
  with members:
    - "some properties of renyi entropy over countably infinite alphabets"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on the hardness of entropy minimization and related problems"
    - "minimum entropy combinatorial optimization problems"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

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)