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: "probabilistic prediction of vehicle semantic intention and motion"
  Abstract: Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving maneuvers. Therefore, a intention prediction method that can adapt to different traffic scenarios is needed. To further improve the overall vehicle prediction performance, motion information is usually incorporated with classified intentions. As suggested in some literature, the methods that directly predict possible goal locations can achieve better performance for long-term motion prediction than other approaches due to their automatic incorporation of environment constraints. Moreover, by obtaining the temporal information of the predicted destinations, the optimal trajectories for predicted vehicles as well as the desirable path for ego autonomous vehicle could be easily generated. In this paper, we propose a Semantic-based Intention and Motion Prediction (SIMP) method, which can be adapted to any driving scenarios by using semantic-defined vehicle behaviors. It utilizes a probabilistic framework based on deep neural network to estimate the intentions, final locations, and the corresponding time information for surrounding vehicles. An exemplar real-world scenario was used to implement and examine the proposed method.

Connected through hyperedge HE-1 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving"):
  with members:
    - "video generation from single semantic label map"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "infer intermediate representations for future prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "starnet pedestrian trajectory prediction using deep neural network in star topology"
    - "peeking into the future predicting future person activities and locations in videos"
    - "video to video synthesis"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal learning of multivehicle interaction patterns in lane change scenarios"):
  with members:
    - "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"
    - "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"
    - "recurrent attentive neural process for sequential data"
    - "a general framework of learning multi vehicle interaction patterns from videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-3 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"

Connected through hyperedge HE-4 (induced by source paper "spatially aware graph neural networks for relational behavior forecasting from sensor data"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "relational action forecasting"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"

Connected through hyperedge HE-6 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "recurrent neural processes"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "the functional neural process"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"

Connected through hyperedge HE-7 (induced by source paper "predicting vehicle behaviors over an extended horizon using behavior interaction network"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-8 (induced by source paper "multi modal probabilistic prediction of interactive behavior via an interpretable model"):
  with members:
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "courteous autonomous cars"

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)