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: "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.

Connected through hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "potential field interpretable and unified representation for trajectory prediction"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-2 (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"
    - "multiple futures 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-3 (induced by source paper "laserflow efficient and probabilistic object detection and motion forecasting"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-4 (induced by source paper "efficient latent representations using multiple tasks for autonomous driving"):
  with members:
    - "urban driving with conditional imitation learning"
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "covernet multimodal behavior prediction using trajectory sets"
    - "pytorch an imperative style high performance deep learning library"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a spatial temporal attentive network with spatial continuity for trajectory prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"

Connected through hyperedge HE-6 (induced by source paper "carpal confidence aware intent recognition for parallel autonomy"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"
    - "safe end to end imitation learning for model predictive control"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "shared autonomy via deep reinforcement learning"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-7 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "energy based continuous inverse optimal control"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "belief and truth in hypothesised behaviours"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "efficient planning under uncertainty with macro actions"

Connected through hyperedge HE-8 (induced by source paper "non gaussian chance constrained trajectory planning for autonomous vehicles in the presence of uncertain agents"):
  with members:
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "provably safe robot navigation with obstacle uncertainty"

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)