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 "multi modal simultaneous forecasting of vehicle position sequences using social attention"):
  Abstract: Vehicle trajectory forecasting models use a wide variety of frameworks for interaction and multi-modality. They rely on various representations of the road scene and definitions of maneuvers. In this paper we present a simple model that simultaneously forecasts each vehicle position on a road scene as a sequence of multi-modal probability density functions. This relies solely on vehicle position tracks and does not define maneuvers. We produce an easily extendable model that combines these predictive capabilities while surpassing state-of-the-art results. Its architecture uses multi-head attention to account for complete interactions between all vehicles, and long short-term memory (LSTM) layers for encoding and forecasting.

Includes members:
  Member "grip graph based interaction aware trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points")
  Member "diverse trajectory forecasting with determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "imitating driver behavior with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")
  Member "car net clairvoyant attentive recurrent network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.LG(Machine Learning)