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: "deep semantic classification for 3d lidar data"
  Abstract: Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.

Connected through hyperedge HE-1 (induced by source paper "deeptemporalseg temporally consistent semantic segmentation of 3d lidar scans"):
  with members:
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "neurall towards a unified model for visual perception in automated driving"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"

Connected through hyperedge HE-3 (induced by source paper "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hallucinating dense optical flow from sparse lidar for autonomous vehicles"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "semantic foggy scene understanding with synthetic data"
    - "optimal sensor data fusion architecture for object detection in adverse weather conditions"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "fast lidar based road detection using fully convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "analyzing the cross sensor portability of neural network architectures for lidar based semantic labeling"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "self supervised generalisation with meta auxiliary learning"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "birdnet a 3d object detection framework from lidar information"

Connected through hyperedge HE-6 (induced by source paper "incorporating human domain knowledge in 3d lidar based semantic segmentation"):
  with members:
    - "semantic segmentation of 3d lidar data in dynamic scene using semi supervised learning"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "incorporating human domain knowledge into large scale cost function learning"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-7 (induced by source paper "domain adaptation for vehicle detection from bird s eye view lidar point cloud data"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "mapless online detection of dynamic objects in 3d lidar"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "yolov3 an incremental improvement"
    - "deep visual domain adaptation a survey"
    - "carla an open urban driving simulator"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "deep generative modeling of lidar data"):
  with members:
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "the relativistic discriminator a key element missing from standard gan"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dscnet replicating lidar point clouds with deep sensor cloning"
    - "deconvolutional networks for point cloud vehicle detection and tracking in driving scenarios"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)