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: "dynamic graph convolutional networks"
  Abstract: Abstract   In many different classification tasks it is required to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change over time. The goal is to exploit existing neural network architectures to model datasets that are best represented with graph structures that change over time. To the best of the authors’ knowledge, this task has not been addressed using these kinds of architectures. Two novel approaches are proposed, which combine Long Short-Term Memory networks and Graph Convolutional Networks to learn long short-term dependencies together with graph structure. The advantage provided by the proposed methods is confirmed by the results achieved on four real world datasets: an increase of up to 12 percentage points in Accuracy and F1 scores for vertex-based semi-supervised classification and up to 2 percentage points in Accuracy and F1 scores for graph-based supervised classification.

Connected through hyperedge HE-1 (induced by source paper "a survey of adversarial learning on graphs"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "multiscale evolutionary perturbation attack on community detection"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "time aware gradient attack on dynamic network link prediction"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "adversarial robustness of similarity based link prediction"
    - "graphsac detecting anomalies in large scale graphs"

Connected through hyperedge HE-2 (induced by source paper "learning to simulate complex physics with graph networks"):
  with members:
    - "learning to control pdes with differentiable physics"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "hamiltonian graph networks with ode integrators"
    - "scale mlperf 0 6 models on google tpu v3 pods"
    - "difftaichi differentiable programming for physical simulation"
    - "graph networks as learnable physics engines for inference and control"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-3 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"
    - "rethinking kernel methods for node representation learning on graphs"

Connected through hyperedge HE-4 (induced by source paper "dynamic graph message passing networks"):
  with members:
    - "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"
    - "adaptive sampling towards fast graph representation learning"

Connected through hyperedge HE-5 (induced by source paper "temporal neighbourhood aggregation predicting future links in temporal graphs via recurrent variational graph convolutions"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "pytorch an imperative style high performance deep learning library"
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "gc lstm graph convolution embedded lstm for dynamic link prediction"
    - "dynamicgem a library for dynamic graph embedding methods"

Connected through hyperedge HE-6 (induced by source paper "tensor graph convolutional networks for prediction on dynamic graphs"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "stable tensor neural networks for rapid deep learning"
    - "community interaction and conflict on the web"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-7 (induced by source paper "evolvegcn evolving graph convolutional networks for dynamic graphs"):
  with members:
    - "graph u nets"
    - "dyngem deep embedding method for dynamic graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "representation learning over dynamic graphs"
    - "attention is all you need"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-8 (induced by source paper "evonet a neural network for predicting the evolution of dynamic graphs"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "dyngem deep embedding method for dynamic graphs"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "how powerful are graph 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.LG(Machine Learning)