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 "visual inertial scene representations"):
  Abstract: We describe a representation of a scene that captures geometric and semantic attributes of objects within, along with their uncertainty. Objects are assumed persistent in the scene, and their likelihood computed from intermittent visual data using a convolutional architecture, integrated within a Bayesian filtering framework with inertials and a context model. Our method yields a posterior estimate of geometry (attributed point cloud and associated uncertainty), semantics (identities and co-occurrence), and a point-estimate of topology for a variable number of objects within the scene, implemented causally and in real-time on commodity hardware.

Includes members:
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "routedfusion learning real time depth map fusion")
  Member "deep learning of local rgb d patches for 3d object detection and 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "subcategory aware convolutional neural networks for object proposals and detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "distributed consistent data association":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks")
  Member "exploring context with deep structured models for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3dcfs fast and robust joint 3d semantic instance segmentation via coupled feature selection")
  Member "semanticfusion dense 3d semantic mapping with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)