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 "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"):
  Abstract: Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multi-modal set of possible outcomes in real-world domains such as autonomous driving. Beyond single MAP trajectory prediction, obtaining an accurate probability distribution of the future is an area of active interest. We present MultiPath, which leverages a fixed set of future state-sequence anchors that correspond to modes of the trajectory distribution. At inference, our model predicts a discrete distribution over the anchors and, for each anchor, regresses offsets from anchor waypoints along with uncertainties, yielding a Gaussian mixture at each time step. Our model is efficient, requiring only one forward inference pass to obtain multi-modal future distributions, and the output is parametric, allowing compact communication and analytical probabilistic queries. We show on several datasets that our model achieves more accurate predictions, and compared to sampling baselines, does so with an order of magnitude fewer trajectories.

Includes members:
  Member "rules of the road predicting driving behavior with a convolutional model of semantic interactions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "precog prediction conditioned on goals in visual multi agent settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "multimodal trajectory predictions for autonomous driving using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "generative modeling of multimodal multi human behavior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings")
  Member "social gan socially acceptable trajectories with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "chauffeurnet learning to drive by imitating the best and synthesizing the worst":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "accurate and diverse sampling of sequences based on a best of many sample objective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "desire distant future prediction in dynamic scenes with interacting agents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

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)