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: "hierarchical attentive recurrent tracking"
  Abstract: Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object. This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods. To improve training convergence, we augment the loss function with terms for a number of auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets: pedestrian tracking on the KTH activity recognition dataset and the more difficult KITTI object tracking dataset.

Connected through hyperedge HE-1 (induced by source paper "scalor generative world models with scalable object representations"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "exploiting spatial invariance for scalable unsupervised object tracking"
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking by animation unsupervised learning of multi object attentive trackers"

Connected through hyperedge HE-2 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

Connected through hyperedge HE-3 (induced by source paper "towards robust image classification using sequential attention models"):
  with members:
    - "natural adversarial examples"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "attention please adversarial defense via attention rectification and preservation"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"

Connected through hyperedge HE-4 (induced by source paper "towards interpretable reinforcement learning using attention augmented agents"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "compositional attention networks for machine reasoning"
    - "cram clued recurrent attention model"
    - "a spatiotemporal model with visual attention for video classification"
    - "attention is all you need"
    - "multi focus attention network for efficient deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-6 (induced by source paper "learning to update for object tracking with recurrent meta learner"):
  with members:
    - "good features to correlate for visual tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "end to end recurrent multi object tracking and trajectory prediction with relational reasoning"):
  with members:
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "deep sets"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"

Connected through hyperedge HE-8 (induced by source paper "conditional generative neural system for probabilistic trajectory prediction"):
  with members:
    - "generic tracking and probabilistic prediction framework and its application in autonomous driving"
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement 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.CV(Computer Vision and Pattern Recognition)