human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "simplifying reward design through divide and conquer"
  Abstract: Designing a good reward function is essential to robot planning and reinforcement learning, but it can also be challenging and frustrating. The reward needs to work across multiple different environments, and that often requires many iterations of tuning. We introduce a novel divide-and-conquer approach that enables the designer to specify a reward separately for each environment. By treating these separate reward functions as observations about the underlying true reward, we derive an approach to infer a common reward across all environments. We conduct user studies in an abstract grid world domain and in a motion planning domain for a 7-DOF manipulator that measure user effort and solution quality. We show that our method is faster, easier to use, and produces a higher quality solution than the typical method of designing a reward jointly across all environments. We additionally conduct a series of experiments that measure the sensitivity of these results to different properties of the reward design task, such as the number of environments, the number of feasible solutions per environment, and the fraction of the total features that vary within each environment. We find that independent reward design outperforms the standard, joint, reward design process but works best when the design problem can be divided into simpler subproblems.

Connected through hyperedge HE-1 (induced by source paper "energy based continuous inverse optimal control"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-2 (induced by source paper "learning reward functions by integrating human demonstrations and preferences"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "reward learning from human preferences and demonstrations in atari"
    - "batch active preference based learning of reward functions"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-3 (induced by source paper "planning with uncertain specifications puns"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "planning with state abstractions for non markovian task specifications"
    - "environment independent task specifications via gltl"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "inverse reward design"
    - "q learning for robust satisfaction of signal temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "m 3 rl mind aware multi agent management reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "universal successor representations for transfer reinforcement learning"
    - "machine theory of mind"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learned optimizers that scale and generalize"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"

Question: Please tell me which class the center node 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)