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: "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
  Abstract: In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples. To this end, we propose to learn a group of capsule subspaces onto which an input feature vector is projected. Then the lengths of resultant capsules are used to score the probability of belonging to different classes. We train such a Capsule Projection Network (CapProNet) by learning an orthogonal projection matrix for each capsule subspace, and show that each capsule subspace is updated until it contains input feature vectors corresponding to the associated class. Only a small negligible computing overhead is incurred to train the network in low-dimensional capsule subspaces or through an alternative hyper-power iteration to estimate the normalization matrix. Experiment results on image datasets show the presented model can greatly improve the performance of state-of-the-art ResNet backbones by $10-20\%$ at the same level of computing and memory costs.

Connected through hyperedge HE-1 (induced by source paper "subspace capsule network"):
  with members:
    - "dual directed capsule network for very low resolution image recognition"
    - "videocapsulenet a simplified network for action detection"
    - "investigating capsule networks with dynamic routing for text classification"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved techniques for training gans"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "capsule routing via variational bayes"):
  with members:
    - "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"
    - "attention routing between capsules"
    - "capsule networks with max min normalization"
    - "the multi lane capsule network mlcn"
    - "videocapsulenet a simplified network for action detection"
    - "group equivariant capsule networks"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "dense and diverse capsule networks making the capsules learn better"

Connected through hyperedge HE-3 (induced by source paper "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "interpretable convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "capsule networks need an improved routing algorithm"):
  with members:
    - "capsnet comparative performance evaluation for image classification"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "adaptive routing between capsules"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "neural network encapsulation"
    - "group equivariant capsule networks"
    - "dynamic routing between capsules"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "3d point capsule networks"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "generalized capsule networks with trainable routing procedure"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

Connected through hyperedge HE-7 (induced by source paper "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "improved explainability of capsule networks relevance path by agreement"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "dropmax adaptive variational softmax"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)