human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "task specific visual saliency prediction with memory augmented conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "tree memory networks for modelling long term temporal dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "two stream lstm a deep fusion framework for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation")
  Member "deep multi camera people detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a bayesian filter for multi view 3d multi object tracking with occlusion handling")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "deep occlusion reasoning for multi camera multi target detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a bayesian filter for multi view 3d multi object tracking with occlusion handling")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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)