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: "multiple futures prediction"
  Abstract: Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others. Towards these goals, we introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene. Our framework is data-driven and learns semantically meaningful latent variables to represent the multimodal future, without requiring explicit labels. Using a dynamic attention-based state encoder, we learn to encode the past as well as the future interactions among agents, efficiently scaling to any number of agents. Finally, our model can be used for planning via computing a conditional probability density over the trajectories of other agents given a hypothetical rollout of the 'self' agent. We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets.

Connected through hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"
    - "scalability in perception for autonomous driving waymo open dataset"

Connected through hyperedge HE-2 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "social and scene aware trajectory prediction in crowded spaces"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "diverse trajectory forecasting with determinantal point processes"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-3 (induced by source paper "the garden of forking paths towards multi future trajectory prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "precog prediction conditioned on goals in visual multi agent settings"

Connected through hyperedge HE-4 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "interpreting the latent space of gans for semantic face editing"
    - "conditional generative neural system for probabilistic trajectory prediction"

Connected through hyperedge HE-5 (induced by source paper "hcnaf hyper conditioned neural autoregressive flow and its application for probabilistic occupancy map forecasting"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

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.LG(Machine Learning)