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 "bounded rational decision making from elementary computations that reduce uncertainty"):
  Abstract: In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou–Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. Consequently, we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures.

Includes members:
  Member "systems of bounded rational agents with information theoretic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems")
  Member "bounded rational decision making with adaptive neural network priors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems")
  Member "a short characterization of relative entropy":
    also connected through other hyperedges:
      (none)
  Member "regularised deep reinforcement learning with guaranteed convergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task generative adversarial nets with shared memory for cross domain coordination control")
  Member "an information theoretic optimality principle for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment")
  Member "information measures the curious case of the binary alphabet":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on a generalization of the jensen shannon divergence")
  Member "a minimum relative entropy principle for learning and acting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient exploration of zero sum stochastic games")
  Member "provably bounded optimal agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the efficiency of human cognition reflects planned information processing")

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)