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: "evolvegcn evolving graph convolutional networks for dynamic graphs"
  Abstract: Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution"):
  with members:
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "representation learning on graphs methods and applications"
    - "know evolve deep reasoning in temporal knowledge graphs"
    - "convolutional 2d knowledge graph embeddings"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "measuring and relieving the over smoothing problem for graph neural networks from the topological view"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "graph neural networks with convolutional arma filters"
    - "fisher bures adversary graph convolutional networks"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-3 (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"
    - "dynamic graph convolutional networks"
    - "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-4 (induced by source paper "tensor graph convolutional networks for prediction on dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "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-5 (induced by source paper "recurrent event network for reasoning over temporal knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "relational inductive biases deep learning and graph networks"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-6 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "demo net degree specific graph neural networks for node and graph classification"

Connected through hyperedge HE-7 (induced by source paper "spectral based graph convolutional network for directed graphs"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "adaptive graph convolutional neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "learning deep generative models of graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-8 (induced by source paper "recurrent event network global structure inference over temporal knowledge graph"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "recurrent relational networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

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)