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: "hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification"
  Abstract: Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.

Connected through hyperedge HE-1 (induced by source paper "poisson kernel avoiding self smoothing in graph convolutional networks"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "simplifying graph convolutional networks"
    - "socialgcn an efficient graph convolutional network based model for social recommendation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-3 (induced by source paper "hierarchical graph pooling with structure learning"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "self attention graph pooling"
    - "ipool information based pooling in hierarchical graph neural networks"
    - "edge contraction pooling for graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-4 (induced by source paper "integrated multi omics analysis using variational autoencoders application to pan cancer classification"):
  with members:
    - "multi view factorization autoencoder with network constraints for multi omic integrative analysis"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)