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 "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"):
  Abstract: Multiple object tracking has been a challenging field, mainly due to noisy detection sets an identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models that are built on an individual or several selected frames for the comparison of features, but they cannot encode the long-term appearance changes caused by pose, viewing angle, and lighting conditions. In this paper, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by the adaptive update and spatial weighting schemes. To handle occlusion and nearby objects that are sharing a similar appearance, we also design the cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. In addition, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state of the art on the  MOTChallenge  3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state of the art on the 2D benchmark of  MOTChallenge .

Includes members:
  Member "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aerial multi object tracking by detection using deep association networks")
  Member "a hybrid data association framework for robust online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "tracklet association by online target specific metric learning and coherent dynamics estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online multiple pedestrian tracking using deep temporal appearance matching association")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")
  Member "mot16 a benchmark for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "online multi target tracking using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "a multi cut formulation for joint segmentation and tracking of multiple objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention")

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)