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 multimodal subspace clustering networks"
  Abstract: We present convolutional neural network based approaches for unsupervised multimodal subspace clustering. The proposed framework consists of three main stages—multimodal encoder, self-expressive layer, and multimodal decoder. The encoder takes multimodal data as input and fuses them to a latent space representation. The self-expressive layer is responsible for enforcing the self-expressiveness property and acquiring an affinity matrix corresponding to the data points. The decoder reconstructs the original input data. The network uses the distance between the decoder's reconstruction and the original input in its training. We investigate early, late, and intermediate fusion techniques and propose three different encoders corresponding to them for spatial fusion. The self-expressive layers and multimodal decoders are essentially the same for different spatial fusion-based approaches. In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same. Extensive experiments on three datasets show that the proposed methods significantly outperform the state-of-the-art multimodal subspace clustering methods.

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"
    - "deep spectral clustering using dual autoencoder 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 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 "variational inference for deep probabilistic canonical correlation analysis"):
  with members:
    - "an introduction to variational autoencoders"
    - "deep variational canonical correlation analysis"
    - "deep subspace clustering networks"
    - "acoustic feature learning via deep variational canonical correlation analysis"
    - "adam a method for stochastic optimization"
    - "manifold relevance determination"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "reducing network agnostophobia"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-4 (induced by source paper "multi view deep subspace clustering networks"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "subspace clustering by block diagonal representation"
    - "simple to complex cross modal learning to rank"
    - "non local neural networks"
    - "deep subspace clustering networks"
    - "deconvolutional paragraph representation learning"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "deep sparse representation based classification"):
  with members:
    - "learning mixtures of separable dictionaries for tensor data analysis and algorithms"
    - "deep subspace clustering networks"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep multi view learning to rank"):
  with members:
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "a multi view embedding space for modeling internet images tags and their semantics"
    - "learning to order things"

Connected through hyperedge HE-7 (induced by source paper "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "deep transfer learning for multiple class novelty detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep coral correlation alignment for deep domain adaptation"

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)