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 "active task inference guided deep inverse reinforcement learning"):
  Abstract: In inverse reinforcement learning (IRL), given a Markov decision process (MDP) and a set of demonstrations for completing a task, the objective is to learn a reward function to explain the demonstrations. However, it is challenging to apply IRL in tasks where a proper memory structure is the key to complete the task. To address this challenge, we develop an iterative algorithm that alternates between a task inference module that infers the high-level memory structure of the task and a reward learning module that learns a reward function with the inferred memory structure. In each iteration, the task inference module produces a series of queries to be answered by the demonstrator. Each query asks whether a sequence of high-level events leads to the completion of the task. The demonstrator then provides a demonstration executing the sequence to answer each query. After the queries are answered, the task inference module returns a hypothesis deterministic finite automaton (DFA) encoding the high-level memory structure to be used by the reward learning. The reward learning module incorporates the DFA states into the MDP states and creates a product automaton. Then the reward learning module proceeds to learn a Markovian reward function for this product automaton by performing deep maximum entropy IRL. At the end of each iteration, the algorithm computes an optimal policy with respect to the learned reward. This iterative process continues until the computed policy leads to satisfactory performance in completing the task. The experiments show that the proposed algorithm outperforms three IRL baselines in task performance.

Includes members:
  Member "taco learning task decomposition via temporal alignment for control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized hierarchical policies for compositional transfer in robotics")
  Member "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions")
  Member "watch this scalable cost function learning for path planning in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "guided cost learning deep inverse optimal control via policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "maximum entropy deep inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "apprenticeship learning using inverse reinforcement learning and gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning navigation costs from demonstration in partially observable environments")

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.RO(Robotics)