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: "graph based convolutional neural network"
  Abstract: The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification. The use of CNNs becomes problematic within the irregular spatial domain due to design and convolution of a kernel filter being non-trivial. One solution to this problem is to utilize graph signal processing techniques and the convolution theorem to perform convolutions on the graph of the irregular domain to obtain feature map responses to learnt filters. We propose graph convolution and pooling operators analogous to those in the regular domain. We also provide gradient calculations on the input data and spectral filters, which allow for the deep learning of an irregular spatial domain problem. Signal filters take the form of spectral multipliers, applying convolution in the graph spectral domain. Applying smooth multipliers results in localized convolutions in the spatial domain, with smoother multipliers providing sharper feature maps. Algebraic Multigrid is presented as a graph pooling method, reducing the resolution of the graph through agglomeration of nodes between layers of the network. Evaluation of performance on the MNIST digit classification problem in both the regular and irregular domain is presented, with comparison drawn to standard CNN. The proposed graph CNN provides a deep learning method for the irregular domains present in the machine learning community, obtaining 94.23% on the regular grid, and 94.96% on a spatially irregular subsampled MNIST.

Connected through hyperedge HE-1 (induced by source paper "nlocalsat boosting local search with solution prediction"):
  with members:
    - "guiding high performance sat solvers with unsat core predictions"
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "repgn object detection with relational proposal graph network"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "sequential context encoding for duplicate removal"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "path aggregation network for instance segmentation"
    - "relation networks for object detection"
    - "yes net an effective detector based on global information"
    - "cascade r cnn delving into high quality object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "gfcn a new graph convolutional network based on parallel flows"):
  with members:
    - "graph normalizing flows"
    - "adaptive graph convolutional neural networks"
    - "topology adaptive graph convolutional networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-4 (induced by source paper "graphflow a new graph convolutional network based on parallel flows"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "topology adaptive graph convolutional networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "graph learning from data under laplacian and structural constraints"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-5 (induced by source paper "graph convolutional neural networks via scattering"):
  with members:
    - "encoding robust representation for graph generation"
    - "covariant compositional networks for learning graphs"
    - "dynamic routing between capsules"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "energy propagation in deep convolutional neural networks"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples on graph data deep insights into attack and defense"):
  with members:
    - "simplifying graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representation learning on graphs with jumping knowledge networks"
    - "attack graph convolutional networks by adding fake nodes"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "building dynamic knowledge graphs from text using machine reading comprehension"):
  with members:
    - "neural architectures for open type relation argument extraction"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "zero shot relation extraction via reading comprehension"
    - "relnet end to end modeling of entities relations"
    - "fasttext zip compressing text classification models"
    - "simulating action dynamics with neural process networks"

Connected through hyperedge HE-8 (induced by source paper "foldingnet point cloud auto encoder via deep grid deformation"):
  with members:
    - "deep sets"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d shape segmentation via shape fully convolutional networks"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

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)