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: "end to end tracking and semantic segmentation using recurrent neural networks"
  Abstract: In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification.

Connected through hyperedge HE-1 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "deepigeos a deep interactive geodesic framework for medical image segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "a survey on deep learning in medical image analysis"
    - "instance sensitive fully convolutional networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "leveraging pretrained image classifiers for language based segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "rtseg real time semantic segmentation comparative study"
    - "taskonomy disentangling task transfer learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "object level representation learning for few shot image classification"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"

Connected through hyperedge HE-4 (induced by source paper "short term prediction and multi camera fusion on semantic grids"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "long term occupancy grid prediction using recurrent neural networks"
    - "deep network uncertainty maps for indoor navigation"
    - "predicting deeper into the future of semantic segmentation"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-5 (induced by source paper "2d lidar map prediction via estimating motion flow with gru"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "video frame synthesis using deep voxel flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "view synthesis by appearance flow"
    - "spatial transformer networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "deep grid net dgn a deep learning system for real time driving context understanding"):
  with members:
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "population based training of neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "online visual robot tracking and identification using deep lstm networks"):
  with members:
    - "a monocular vision system for playing soccer in low color information environments"
    - "real time visual tracking and identification for a team of homogeneous humanoid robots"
    - "child sized 3d printed igus humanoid open platform"
    - "lstm a search space odyssey"
    - "robust and real time deep tracking via multi scale domain adaptation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "globally consistent multi people tracking using motion patterns"
    - "deep tracking seeing beyond seeing 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.LG(Machine Learning)