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 "scenario generalization of data driven imitation models in crowd simulation"):
  Abstract: Crowd simulation, the study of the movement of multiple agents in complex environments, presents a unique application domain for machine learning. One challenge in crowd simulation is to imitate the movement of expert agents in highly dense crowds. An imitation model could substitute an expert agent if the model behaves as good as the expert. This will bring many exciting applications. However, we believe no prior studies have considered the critical question of how training data and training methods affect imitators when these models are applied to novel scenarios. In this work, a general imitation model is represented by applying either the Behavior Cloning (BC) training method or a more sophisticated Generative Adversarial Imitation Learning (GAIL) method, on three typical types of data domains: standard benchmarks for evaluating crowd models, random sampling of state-action pairs, and egocentric scenarios that capture local interactions. Simulated results suggest that (i) simpler training methods are overall better than more complex training methods, (ii) training samples with diverse agent-agent and agent-obstacle interactions are beneficial for reducing collisions when the trained models are applied to new scenarios. We additionally evaluated our models in their ability to imitate real world crowd trajectories observed from surveillance videos. Our findings indicate that models trained on representative scenarios generalize to new, unseen situations observed in real human crowds.

Includes members:
  Member "theoretical perspective of deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of domain adaptation without target labels")
  Member "a survey of inverse reinforcement learning challenges methods and progress":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations")
  Member "on deep domain adaptation some theoretical understandings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a review of domain adaptation without target labels")
  Member "behavioral cloning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "bayesian optimization with automatic prior selection for data efficient direct policy search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors")
  Member "the role of data driven priors in multi agent crowd trajectory estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization")
  Member "imitating driver behavior with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")
  Member "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robot navigation with map based deep reinforcement learning")

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.MA(Multiagent Systems)