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: "self driving cars a survey"
  Abstract: We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-2 (induced by source paper "deep neural network perception models and robust autonomous driving systems"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "one pixel attack for fooling deep neural networks"
    - "adversarially robust distillation"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-3 (induced by source paper "overview of tools supporting planning for automated driving"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "design space of behaviour planning for autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "control of connected and automated vehicles state of the art and future challenges"

Connected through hyperedge HE-4 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "a parametric top view representation of complex road scenes"
    - "pixels to graphs by associative embedding"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "long term prediction of vehicle behavior using short term uncertainty aware trajectories and high definition maps"):
  with members:
    - "human motion trajectory prediction 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-6 (induced by source paper "scheduling observers over a shared channel with hard delivery deadlines"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "joint scheduling of urllc and embb traffic in 5g wireless networks"

Connected through hyperedge HE-7 (induced by source paper "efficient motion planning for automated lane change based on imitation learning and mixed integer optimization"):
  with members:
    - "a data driven method of optimizing feedforward compensator for autonomous vehicle"
    - "learning a lattice planner control set for autonomous vehicles"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-8 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "robust and high performance face detector"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"

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)