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 "factorized mutual information maximization"):
  Abstract: We investigate the sets of joint probability distributions that maximize the average multi-information over a collection of margins. These functionals serve as proxies for maximizing the multi-information of a set of variables or the mutual information of two subsets of variables, at a lower computation and estimation complexity. We describe the maximizers and their relations to the maximizers of the multi-information and the mutual information.

Includes members:
  Member "large scale study of curiosity driven learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deep variational information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "information theoretically aided reinforcement learning for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mega reward achieving human level play without extrinsic rewards")
  Member "a theory of cheap control in embodied systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nmode neuro module evolution")
  Member "efficient estimation of mutual information for strongly dependent variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "multivariate information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamics aware unsupervised discovery of skills")
  Member "finding the maximizers of the information divergence from an exponential family":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic feedforward neural networks universal approximation")
  Member "higher coordination with less control a result of information maximization in the sensorimotor loop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment")

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)