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: "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
  Abstract: We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory of each road-agent. Our formulation represents the proximity between the road agents using a dynamic weighted traffic-graph. We use a two-stream graph convolutional LSTM network to perform traffic forecasting using these weighted traffic-graphs. The first stream predicts the spatial coordinates of road-agents, while the second stream predicts whether a road-agent is going to exhibit aggressive, conservative, or normal behavior. We introduce spectral cluster regularization to reduce the error margin in long-term prediction (3-5 seconds) and improve the accuracy of the predicted trajectories. We evaluate our approach on the Argoverse, Lyft, and Apolloscape datasets and highlight the benefits over prior trajectory prediction methods. In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91.2% for behavior prediction.

Connected through hyperedge HE-1 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "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 "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"
    - "multiple futures prediction"
    - "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 "cmetric a driving behavior measure using centrality functions"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "graphrqi classifying driver behaviors using graph spectrums"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "identifying driver behaviors using trajectory features for vehicle navigation"

Connected through hyperedge HE-4 (induced by source paper "graphrqi classifying driver behaviors using graph spectrums"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "self driving cars a survey"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "roadtrack realtime tracking of road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "a hybrid data association framework for robust online multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "roadtrack tracking road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "a hybrid data association framework for robust online multi object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

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)