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: "on the sample complexity of end to end training vs semantic abstraction training"
  Abstract: We compare the end-to-end training approach to a modular approach in which a system is decomposed into semantically meaningful components. We focus on the sample complexity aspect, in the regime where an extremely high accuracy is necessary, as is the case in autonomous driving applications. We demonstrate cases in which the number of training examples required by the end-to-end approach is exponentially larger than the number of examples required by the semantic abstraction approach.

Connected through hyperedge HE-1 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "improved semantic stixels via multimodal sensor fusion"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-2 (induced by source paper "f1 10 an open source autonomous cyber physical platform"):
  with members:
    - "decentralized mpc based obstacle avoidance for multi robot target tracking scenarios"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "learning to drive in a day"
    - "sequential circuits from regular expressions revisited"
    - "montre a tool for monitoring timed regular expressions"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cddt fast approximate 2d ray casting for accelerated localization"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-3 (induced by source paper "closing the gap towards end to end autonomous vehicle system"):
  with members:
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "deep density networks and uncertainty in recommender systems"
    - "end to end multi modal multi task vehicle control for self driving cars with visual perception"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "focal loss for dense object detection"
    - "failures of gradient based deep learning"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-4 (induced by source paper "recurrent flow guided semantic forecasting"):
  with members:
    - "nonlinear 3d face morphable model"
    - "dual motion gan for future flow embedded video prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "flow guided feature aggregation for video object detection"
    - "unsupervised learning of disentangled representations from video"
    - "pose invariant face alignment with a single cnn"

Connected through hyperedge HE-5 (induced by source paper "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "person following by autonomous robots a categorical overview"
    - "learning to navigate autonomously in outdoor environments mavnet"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "limits of end to end learning"
    - "learning to fly by crashing"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"

Connected through hyperedge HE-6 (induced by source paper "future segmentation using 3d structure"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of depth and ego motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "long term planning by short term prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "video scene parsing with predictive feature learning"

Connected through hyperedge HE-7 (induced by source paper "discovery and usage of joint attention in images"):
  with members:
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "very deep convolutional networks for large scale image recognition"
    - "deep visual semantic alignments for generating image descriptions"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "predicting deeper into the future of semantic segmentation"):
  with members:
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "unsupervised learning of long term motion dynamics for videos"
    - "transformation based models of video sequences"
    - "unsupervised learning for physical interaction through video prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "long term planning by short term prediction"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "video scene parsing with predictive feature learning"

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.LG(Machine Learning)