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 "instance wise depth and motion learning from monocular videos"):
  Abstract: We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. The only annotation used in our pipeline is a video instance segmentation map that can be predicted by our new auto-annotation scheme. Our technical contributions are three-fold. First, we propose a differentiable forward rigid projection module that plays a key role in our instance-wise depth and motion learning. Second, we design an instance-wise photometric and geometric consistency loss that effectively decomposes background and moving object regions. Lastly, we introduce an instance-wise mini-batch re-arrangement scheme that does not require additional iterations in training. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods.

Includes members:
  Member "learning residual flow as dynamic motion from stereo videos":
    also connected through other hyperedges:
      (none)
  Member "semantickitti a dataset for semantic scene understanding of lidar sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "learning independent object motion from unlabelled stereoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised object motion and depth estimation from video")
  Member "self supervised learning with geometric constraints in monocular video connecting flow depth and camera":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "video instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "visuomotor understanding for representation learning of driving scenes":
    also connected through other hyperedges:
      (none)
  Member "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "flowfusion dynamic dense rgb d slam based on optical flow")
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)