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: "tracking by prediction a deep generative model for mutli person localisation and tracking"
  Abstract: Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.

Connected through hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "instance aware representation learning and association for online multi person tracking"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "towards multi object detection and tracking in urban scenario under uncertainties"

Connected through hyperedge HE-2 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "multiple object forecasting predicting future object locations in diverse environments"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "looking to relations for future trajectory forecast"
    - "online multi object tracking with dual matching attention networks"
    - "forecasting pedestrian trajectory with machine annotated training data"
    - "peeking into the future predicting future person activities and locations in videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "fine grained action segmentation using the semi supervised action gan"):
  with members:
    - "robust visual tracking using multi frame multi feature joint modeling"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "a turing test for crowds"):
  with members:
    - "vadere an open source simulation framework to promote interdisciplinary understanding"
    - "globally continuous and non markovian crowd activity analysis from videos"

Connected through hyperedge HE-7 (induced by source paper "learning temporal strategic relationships using generative adversarial imitation learning"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "simultaneous policy learning and latent state inference for imitating driver behavior"
    - "imitating driver behavior with generative adversarial networks"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "third person imitation learning"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-8 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "structural recurrent neural network srnn for group activity analysis"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "cern confidence energy recurrent network for group activity recognition"
    - "pixel level domain transfer"

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)