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 "irs a large synthetic indoor robotics stereo dataset for disparity and surface normal estimation"):
  Abstract: Indoor robotics localization, navigation and interaction heavily rely on scene understanding and reconstruction. Compared to monocular vision which usually does not explicitly introduce any geometrical constraint, stereo vision based schemes are more promising and robust to produce accurate geometrical information, such as surface normal and depth/disparity. Besides, deep learning models trained with large-scale datasets have shown their superior performance in many stereo vision tasks. However, existing stereo datasets rarely contain the high-quality surface normal and disparity ground truth, which hardly satisfy the demand of training a prospective deep model for indoor scenes. To this end, we introduce a large-scale synthetic indoor robotics stereo (IRS) dataset with over 100K stereo RGB images and high-quality surface normal and disparity maps. Leveraging the advanced rendering techniques of our customized rendering engine, the dataset is considerably close to the real-world captured images and covers several visual effects, such as brightness changes, light reflection/transmission, lens flare, vivid shadow, etc. We compare the data distribution of IRS with existing stereo datasets to illustrate the typical visual attributes of indoor scenes. In addition, we present a new deep model DispNormNet to simultaneously infer surface normal and disparity from stereo images. Compared to existing models trained on other datasets, DispNormNet trained with IRS produces much better estimation of surface normal and disparity for indoor scenes.

Includes members:
  Member "deep surface normal estimation with hierarchical rgb d fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data")
  Member "ga net guided aggregation net for end to end stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "group wise correlation stereo network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "fast disparity estimation using dense networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast depth estimation for view synthesis")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "semi global stereo matching with surface orientation priors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep depth prior for multi view stereo")
  Member "single shot object detection with enriched semantics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "proposal learning for semi supervised object detection")

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)