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 "selectfusion a generic framework to selectively learn multisensory fusion"):
  Abstract: Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g. locations and orientations. Although deep learning approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion - a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models also suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module which can be applied to useful pairs of sensor modalities such as monocular images and inertial measurements, depth images and LIDAR point clouds. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and estimate both trajectory at scale and global pose. In particular, we propose two fusion modules based on different attention strategies: deterministic soft fusion and stochastic hard fusion, and we offer a comprehensive study of the new strategies compared to trivial direct fusion. We evaluate all fusion strategies in both ideal conditions and on progressively degraded datasets that present occlusions, noisy and missing data and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself, provides insights into the operation of the various models.

Includes members:
  Member "beyond tracking selecting memory and refining poses for deep visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential adversarial learning for self supervised deep visual odometry")
  Member "selective sensor fusion for neural visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "local supports global deep camera relocalization with sequence enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "lo net deep real time lidar odometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based wearable assistive system for visually impaired people")
  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")
  Member "codeslam learning a compact optimisable representation for dense visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "the limits and potentials of deep learning for robotics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")

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)