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 "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  Abstract: It is an effective strategy for the multi-person pose tracking task in videos to employ prediction and pose matching in a frame-by-frame manner. For this type of approach, uncertainty-aware modeling is essential because precise prediction is impossible. However, previous studies have relied on only a single prediction without incorporating uncertainty, which can cause critical tracking errors if the prediction is unreliable. This paper proposes an extension to this approach with Sequential Monte Carlo (SMC). This naturally reformulates the tracking scheme to handle multiple predictions (or hypotheses) of poses, thereby mitigating the negative effect of prediction errors. An important component of SMC, i.e., a proposal distribution, is designed as a probabilistic neural pose predictor, which can propose diverse and plausible hypotheses by incorporating epistemic uncertainty and heteroscedastic aleatoric uncertainty. In addition, a recurrent architecture is introduced to our neural modeling to utilize time-sequence information of poses to manage difficult situations, such as the frequent disappearance and reappearances of poses. Compared to existing baselines, the proposed method achieves a state-of-the-art MOTA score on the PoseTrack2018 validation dataset by reducing approximately 50% of tracking errors from a state-of-the art baseline method.

Includes members:
  Member "pose estimator and tracker using temporal flow maps for limbs":
    also connected through other hyperedges:
      (none)
  Member "variational inference mpc for bayesian model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning through active inference")
  Member "uncertainty aware imitation learning using kernelized movement primitives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards orientation learning and adaptation in cartesian space")
  Member "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks":
    also connected through other hyperedges:
      (none)
  Member "beyond pixels leveraging geometry and shape cues for online multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset")
  Member "multi domain pose network for multi person pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning temporal pose estimation from sparsely labeled videos")
  Member "pose flow efficient online pose tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "a fusion approach for multi frame optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scopeflow dynamic scene scoping for optical flow")

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)