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: "gresnet graph residual network for reviving deep gnns from suspended animation"
  Abstract: The existing graph neural networks (GNNs) based on the spectral graph convolutional operator have been criticized for its performance degradation, which is especially common for the models with deep architectures. In this paper, we further identify the suspended animation problem with the existing GNNs. Such a problem happens when the model depth reaches the suspended animation limit, and the model will not respond to the training data any more and become not learnable. Analysis about the causes of the suspended animation problem with existing GNNs will be provided in this paper, whereas several other peripheral factors that will impact the problem will be reported as well. To resolve the problem, we introduce the GResNet (Graph Residual Network) framework in this paper, which creates extensively connected highways to involve nodes' raw features or intermediate representations throughout the graph for all the model layers. Different from the other learning settings, the extensive connections in the graph data will render the existing simple residual learning methods fail to work. We prove the effectiveness of the introduced new graph residual terms from the norm preservation perspective, which will help avoid dramatic changes to the node's representations between sequential layers. Detailed studies about the GResNet framework for many existing GNNs, including GCN, GAT and LoopyNet, will be reported in the paper with extensive empirical experiments on real-world benchmark datasets.

Connected through hyperedge HE-1 (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"
    - "graph star net for generalized multi task learning"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (induced by source paper "graph neural distance metric learning with graph bert"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "graph bert only attention is needed for learning graph representations"
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "structure fusion based on graph convolutional networks for semi supervised classification"
    - "wasserstein weisfeiler lehman graph kernels"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-3 (induced by source paper "segmented graph bert for graph instance modeling"):
  with members:
    - "graph bert only attention is needed for learning graph representations"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dagcn dual attention graph convolutional networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "capsule neural networks for graph classification using explicit tensorial graph representations"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (induced by source paper "get rid of suspended animation problem deep diffusive neural network on graph semi supervised classification"):
  with members:
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep loopy neural network model for graph structured data representation learning"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-5 (induced by source paper "graph bert only attention is needed for learning graph representations"):
  with members:
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "transformer xl attentive language models beyond a fixed length context"
    - "segen sample ensemble genetic evolutionary network model"
    - "rethinking imagenet pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep loopy neural network model for graph structured data representation learning"
    - "junction tree variational autoencoder for molecular graph generation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "fast and deep graph neural networks"
    - "a differentiable programming system to bridge machine learning and scientific computing"

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.LG(Machine Learning)