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 tactile fusion object clustering"):
  Abstract: Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensivelystudied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align thevisual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework.

Includes members:
  Member "representative task self selection for flexible clustered lifelong learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lifelong spectral clustering")
  Member "semantic transferable weakly supervised endoscopic lesions segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointdan a multi scale 3d domain adaption network for point cloud representation")
  Member "lifelong spectral clustering":
    also connected through other hyperedges:
      (none)
  Member "deep spectral clustering using dual autoencoder network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "connecting touch and vision via cross modal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics")
  Member "3d shape perception from monocular vision touch and shape priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks")
  Member "connecting look and feel associating the visual and tactile properties of physical materials":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal deep learning for robot touch")
  Member "active clothing material perception using tactile sensing and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tactile based insertion for dense box packing")

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)