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: "multi agent imitation learning for driving simulation"
  Abstract: Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise because observations at training and test time are sampled from different distributions. This difference makes such models unsuitable for the simulation of driving scenes, where multiple agents must interact realistically over long time horizons. We extend GAIL to address these shortcomings through a parameter-sharing approach grounded in curriculum learning. Compared with single-agent GAIL policies, policies generated by our PS-GAIL method prove superior at interacting stably in a multi-agent setting and capturing the emergent behavior of human drivers.

Connected through hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-2 (induced by source paper "how do we move learning to simulate with system dynamics"):
  with members:
    - "challenges of real world reinforcement learning"
    - "peeking into the future predicting future person activities and locations in videos"
    - "inferring metapopulation propagation network for intra city epidemic control and prevention"
    - "multi agent generative adversarial imitation learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "learning to simulate human movement"):
  with members:
    - "social and scene aware trajectory prediction in crowded spaces"
    - "peeking into the future predicting future person activities and locations in videos"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "inferring metapopulation propagation network for intra city epidemic control and prevention"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (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"
    - "simplifying reward design through divide and conquer"

Connected through hyperedge HE-5 (induced by source paper "independent generative adversarial self imitation learning in cooperative multiagent systems"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "self imitation learning"
    - "multi agent generative adversarial imitation learning"
    - "towards cooperation in sequential prisoner s dilemmas a deep multiagent reinforcement learning approach"
    - "learning from demonstration in the wild"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "counterfactual multi agent policy gradients"
    - "guided deep reinforcement learning for swarm systems"

Connected through hyperedge HE-6 (induced by source paper "interaction aware kalman neural networks for trajectory prediction"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "baidu apollo em motion planner"
    - "imitating driver behavior with generative adversarial networks"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"

Connected through hyperedge HE-7 (induced by source paper "inferring and learning multi robot policies by observing an expert"):
  with members:
    - "multi robot deep reinforcement learning with macro actions"
    - "a reinforcement learning framework for sequencing multi robot behaviors"
    - "resilience by reconfiguration exploiting heterogeneity in robot teams"
    - "a sequential composition framework for coordinating multi robot behaviors"
    - "fault tolerant control for networked mobile robots"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "time contrastive networks self supervised learning from video"
    - "the robotarium a remotely accessible swarm robotics research testbed"

Connected through hyperedge HE-8 (induced by source paper "robust online model adaptation by extended kalman filter with exponential moving average and dynamic multi epoch strategy"):
  with members:
    - "human motion trajectory prediction a survey"
    - "adaptable human intention and trajectory prediction for human robot collaboration"
    - "human motion prediction using semi adaptable neural networks"
    - "recent advances in recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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.AI(Artificial Intelligence)