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: "adaptive sampling towards fast graph representation learning"
  Abstract: Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.

Connected through hyperedge HE-1 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"

Connected through hyperedge HE-2 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "graph neural networks with convolutional arma filters"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "link prediction based on graph neural networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adaptive graph convolutional neural networks"
    - "dual primal graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "pairnorm tackling oversmoothing in gnns"
    - "graphsaint graph sampling based inductive learning method"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph star net for generalized multi task learning"

Connected through hyperedge HE-4 (induced by source paper "self supervised graph representation learning via global context prediction"):
  with members:
    - "self supervised dialogue learning"
    - "gmnn graph markov neural networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "attributed graph clustering via adaptive graph convolution"
    - "graph wavelet neural network"
    - "grasp2vec learning object representations from self supervised grasping"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"

Connected through hyperedge HE-5 (induced by source paper "hygcn a gcn accelerator with hybrid architecture"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-6 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms"):
  with members:
    - "automatic compiler based fpga accelerator for cnn training"
    - "graphsaint graph sampling based inductive learning method"
    - "link prediction based on graph neural networks"
    - "accurate efficient and scalable graph embedding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic graph message passing networks"):
  with members:
    - "dynamic graph convolutional networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-8 (induced by source paper "gnn explainer a tool for post hoc explanation of graph neural networks"):
  with members:
    - "position aware graph neural networks"
    - "explaine an approach for explaining network embedding based link predictions"
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representer point selection for explaining deep neural networks"

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)