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: "human motion trajectory prediction a survey"
  Abstract: With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Connected through hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-2 (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"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "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-3 (induced by source paper "machine learning approaches for motor learning a short review"):
  with members:
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "motion prediction with recurrent neural network dynamical models and trajectory optimization"
    - "generative adversarial self imitation learning"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "convolutional sequence to sequence model for human dynamics"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-4 (induced by source paper "transformer networks for trajectory forecasting"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "generating long sequences with sparse transformers"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-5 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-6 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing"):
  with members:
    - "towards federated learning at scale system design"
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "on exact computation with an infinitely wide neural net"
    - "wireless traffic prediction with scalable gaussian process framework algorithms and verification"
    - "federated machine learning concept and applications"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-7 (induced by source paper "long term prediction of vehicle behavior using short term uncertainty aware trajectories and high definition maps"):
  with members:
    - "self driving cars a survey"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "naturalistic driver intention and path prediction using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "path planning in dynamic environments using generative rnns and monte carlo tree search"):
  with members:
    - "predicting responses to a robot s future motion using generative recurrent neural networks"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "social attention modeling attention in human crowds"

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.RO(Robotics)