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 "reward engineering for object pick and place training"):
  Abstract: Robotic grasping is a crucial area of research as it can result in the acceleration of the automation of several Industries utilizing robots ranging from manufacturing to healthcare. Reinforcement learning is the field of study where an agent learns a policy to execute an action by exploring and exploiting rewards from an environment. Reinforcement learning can thus be used by the agent to learn how to execute a certain task, in our case grasping an object. We have used the Pick and Place environment provided by OpenAI's Gym to engineer rewards. Hindsight Experience Replay (HER) has shown promising results with problems having a sparse reward. In the default configuration of the OpenAI baseline and environment the reward function is calculated using the distance between the target location and the robot end-effector. By weighting the cost based on the distance of the end-effector from the goal in the x,y and z-axes we were able to almost halve the learning time compared to the baselines provided by OpenAI, an intuitive strategy that further reduced learning time. In this project, we were also able to introduce certain user desired trajectories in the learnt policies (city-block / Manhattan trajectories). This helps us understand that by engineering the rewards we can tune the agent to learn policies in a certain way even if it might not be the most optimal but is the desired manner.

Includes members:
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)