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 "deep neural networks with auxiliary model regulated gating for resilient multi modal sensor fusion"):
  Abstract: Deep neural networks allow for fusion of high-level features from multiple modalities and have become a promising end-to-end solution for multi-modal sensor fusion. While the recently proposed gating architectures improve the conventional fusion mechanisms employed in CNNs, these models are not always resilient particularly under the presence of sensor failures. This paper shows that the existing gating architectures fail to robustly learn the fusion weights that critically gate different modalities, leading to the issue of fusion weight inconsistency. We propose a new gating architecture by incorporating an auxiliary model to regularize the main model such that the fusion weight for each sensory modality can be robustly learned. As a result, this new auxiliary-model regulated architecture and its variants outperform the existing non-gating and gating fusion architectures under both clean and corrupted sensory inputs resulted from sensor failures. The obtained performance gains are rather significant in the latter case.

Includes members:
  Member "robust deep multi modal learning based on gated information fusion network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trained model fusion for object detection using gating network")
  Member "joint 3d proposal generation and object detection from view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "sensor fusion for robot control through deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepqoe a unified framework for learning to predict video qoe")
  Member "choosing smartly adaptive multimodal fusion for object detection in changing environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "brain4cars car that knows before you do via sensory fusion deep learning architecture":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "driving datasets literature review")
  Member "multi view 3d object detection network for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "unstructured human activity detection from rgbd images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "human action recognition and assessment via deep neural network self organization")

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.LG(Machine Learning)