human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep spectral clustering using dual autoencoder network"
  Abstract: The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering. We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering. As such the learned latent representations can be more robust to noise. Then the mutual information estimation is utilized to provide more discriminative information from the inputs. Furthermore, a deep spectral clustering method is applied to embed the latent representations into the eigenspace and subsequently clusters them, which can fully exploit the relationship between inputs to achieve optimal clustering results. Experimental results on benchmark datasets show that our method can significantly outperform state-of-the-art clustering approaches.

Connected through hyperedge HE-1 (induced by source paper "multi level representation learning for deep subspace clustering"):
  with members:
    - "self supervised convolutional subspace clustering network"
    - "neural collaborative subspace clustering"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep multimodal subspace clustering networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"

Connected through hyperedge HE-2 (induced by source paper "solving raven s progressive matrices with neural networks"):
  with members:
    - "learning perceptual inference by contrasting"
    - "raven a dataset for relational and analogical visual reasoning"
    - "explainable video action reasoning via prior knowledge and state transitions"
    - "rethinking imagenet pre training"
    - "from recognition to cognition visual commonsense reasoning"
    - "deep clustering for unsupervised learning of visual features"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "clustering by directly disentangling latent space"):
  with members:
    - "a two step disentanglement method"
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "y autoencoders disentangling latent representations via sequential encoding"
    - "from variational to deterministic autoencoders"
    - "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "lstms can capture information beyond order"):
  with members:
    - "orderless recurrent models for multi label classification"
    - "multiple instance learning with graph neural networks"
    - "attention based deep multiple instance learning"
    - "learning and interpreting multi multi instance learning networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual motion gan for future flow embedded video prediction"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-5 (induced by source paper "visual tactile fusion object clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "semantic transferable weakly supervised endoscopic lesions segmentation"
    - "lifelong spectral clustering"
    - "connecting touch and vision via cross modal prediction"
    - "3d shape perception from monocular vision touch and shape priors"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "active clothing material perception using tactile sensing and deep learning"
    - "deep learning for tactile understanding from visual and haptic data"

Question: Please tell me which class the center node 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)