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: "convolution in convolution for network in network"
  Abstract: Network in Netwrok (NiN) is an effective instance and an important extension of Convolutional Neural Network (CNN) consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow MultiLayer Perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and $ 1\times 1 $ convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition rate. However, MLP itself consists of fully connected layers which give rise to a large number of parameters. In this paper, we propose to replace dense shallow MLP with sparse shallow MLP. One or more layers of the sparse shallow MLP are sparely connected in the channel dimension or channel-spatial domain. The proposed method is implemented by applying unshared convolution across the channel dimension and applying shared convolution across the spatial dimension in some computational layers. The proposed method is called CiC. Experimental results on the CIFAR10 dataset, augmented CIFAR10 dataset, and CIFAR100 dataset demonstrate the effectiveness of the proposed CiC method.

Connected through hyperedge HE-1 (induced by source paper "semi heterogeneous three way joint embedding network for sketch based image retrieval"):
  with members:
    - "person re identification by semantic region representation and topology constraint"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning deep sketch abstraction"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "harmonious attention network for person re identification"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "asymmetric feature maps with application to sketch based retrieval"

Connected through hyperedge HE-2 (induced by source paper "learning multilayer channel features for pedestrian detection"):
  with members:
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "how far are we from solving pedestrian detection"
    - "scale aware pixelwise object proposal networks"
    - "scale aware fast r cnn for pedestrian detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "filtered channel features for pedestrian detection"

Connected through hyperedge HE-3 (induced by source paper "transductive zero shot learning with adaptive structural embedding"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "semi supervised zero shot learning by a clustering based approach"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"

Connected through hyperedge HE-4 (induced by source paper "simultaneously learning neighborship and projection matrix for supervised dimensionality reduction"):
  with members:
    - "vectorial dimension reduction for tensors based on bayesian inference"
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "cascaded subpatch networks for effective cnns"
    - "deep linear discriminant analysis"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

Connected through hyperedge HE-5 (induced by source paper "cascaded subpatch networks for effective cnns"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "fitnets hints for thin deep nets"

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)