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: "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
  Abstract: Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation.

Connected through hyperedge HE-1 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "point cloud processing via recurrent set encoding"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"

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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "measuring the utilization of public open spaces by deep learning a benchmark study at the detroit riverfront"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fcos fully convolutional one stage object detection"
    - "object detection with deep learning a review"
    - "detecting and recognizing human object interactions"
    - "multiple human parsing in the wild"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "exploiting edge features in graph neural networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-5 (induced by source paper "a deep learning framework for simulation and defect prediction applied in microelectronics"):
  with members:
    - "fault diagnosis in microelectronics attachment via deep learning analysis of 3 d laser scans"
    - "prediction of industrial process parameters using artificial intelligence algorithms"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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-7 (induced by source paper "dynamic environment prediction in urban scenes using recurrent representation learning"):
  with members:
    - "2d lidar map prediction via estimating motion flow with gru"
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "multi step prediction of occupancy grid maps with recurrent neural networks"
    - "stochastic variational video prediction"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "motion estimation in occupancy grid maps in stationary settings using recurrent neural networks"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "2d lidar map prediction via estimating motion flow with gru"
    - "self supervised flow estimation using geometric regularization with applications to camera image and grid map sequences"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "long term occupancy grid prediction using recurrent neural networks"

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)