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 "learning to simulate human movement"):
  Abstract: Modeling how human moves on the space is useful for policy-making in transportation, public safety, and public health. The human movements can be viewed as a dynamic process that human transits between states (e.g., locations) over time. In the human world where both intelligent agents like humans or vehicles with human drivers play an important role, the states of agents mostly describe human activities, and the state transition is influenced by both the human decisions and physical constraints from the real-world system (e.g., agents need to spend time to move over a certain distance). Therefore, the modeling of state transition should include the modeling of the agent's decision process and the physical system dynamics. In this paper, we propose to model state transition in human movement through learning decision model and integrating system dynamics. In experiments on real-world datasets, we demonstrate that the proposed method can achieve superior performance against the state-of-the-art methods in predicting the next state and generating long-term future states.

Includes members:
  Member "social and scene aware trajectory prediction in crowded spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle")
  Member "peeking into the future predicting future person activities and locations in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "understanding human behaviors in crowds by imitating the decision making process":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision")
  Member "inferring metapopulation propagation network for intra city epidemic control and prevention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how do we move learning to simulate with system dynamics")
  Member "multi agent imitation learning for driving simulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "learning robust rewards with adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)