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 convolutional decision jungle for image classification"
  Abstract: We propose a novel method called deep convolutional decision jungle (CDJ) and its learning algorithm for image classification. The CDJ maintains the structure of standard convolutional neural networks (CNNs), i.e. multiple layers of multiple response maps fully connected. Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s.t. each data sample travels via a specific soft route of those activated nodes. The proposed method CDJ automatically learns features, whereas decision forests and jungles require pre-defined feature sets. Compared to CNNs, the method embeds the benefits of using data-dependent discriminative functions, which better handles multi-modal/heterogeneous data; further,the method offers more diverse sparse network responses, which in turn can be used for cost-effective learning/classification. The network is learnt by combining conventional softmax and proposed entropy losses in each layer. The entropy loss,as used in decision tree growing, measures the purity of data activation according to the class label distribution. The back-propagation rule for the proposed loss function is derived from stochastic gradient descent (SGD) optimization of CNNs. We show that our proposed method outperforms state-of-the-art methods on three public image classification benchmarks and one face verification dataset. We also demonstrate the use of auxiliary data labels, when available, which helps our method to learn more discriminative routing and representations and leads to improved classification.

Connected through hyperedge HE-1 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "ensemble soft margin softmax loss for image classification"
    - "class2str end to end latent hierarchy learning"
    - "do convolutional neural networks learn class hierarchy"
    - "deep forest towards an alternative to deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "training decision trees as replacement for convolution layers"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "splinenets continuous neural decision graphs"
    - "style aggregated network for facial landmark detection"
    - "neural ordinary differential equations"
    - "conditional information gain networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "decision forests convolutional networks and the models in between"

Connected through hyperedge HE-3 (induced by source paper "conditional information gain networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "blockout dynamic model selection for hierarchical deep networks"
    - "decision forests convolutional networks and the models in between"

Connected through hyperedge HE-4 (induced by source paper "learning to generate filters for convolutional neural networks"):
  with members:
    - "learning feed forward one shot learners"
    - "decision forests convolutional networks and the models in between"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

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.CV(Computer Vision and Pattern Recognition)