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 "automatic induction of bellman error features for probabilistic planning"):
  Abstract: Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provided, domain-independent algorithms such as approximate value iteration can learn weighted combinations of those features that often perform well as heuristic estimates of state value (e.g., distance to the goal). Successful applications in real-world domains often require features crafted by human experts. Here, we propose automatic processes for learning useful domain-specific feature sets with little or no human intervention. Our methods select and add features that describe state-space regions of high inconsistency in the Bellman equation (statewise Bellman error) during approximate value iteration. Our method can be applied using any real-valued-feature hypothesis space and corresponding learning method for selecting features from training sets of state-value pairs. We evaluate the method with hypothesis spaces defined by both relational and propositional feature languages, using nine probabilistic planning domains. We show that approximate value iteration using a relational feature space performs at the state-of-the-art in domain-independent stochastic relational planning. Our method provides the first domain-independent approach that plays Tetris successfully (without human-engineered features).

Includes members:
  Member "exploiting first order regression in inductive policy selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a heuristic search algorithm for solving first order mdps")
  Member "inductive policy selection for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural logic reinforcement learning")
  Member "practical linear value approximation techniques for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a structured prediction approach for generalization in cooperative multi agent reinforcement learning")
  Member "flucap a heuristic search planner for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the complexity of reasoning with fodd and gfodd")
  Member "approximate policy iteration with a policy language bias solving relational markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized neural policies for relational mdps")
  Member "the automatic inference of state invariants in tim":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper")
  Member "learning first order definitions of functions":
    also connected through other hyperedges:
      (none)

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.AI(Artificial Intelligence)