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: "representation learning over dynamic graphs"
  Abstract: How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction.

Connected through hyperedge HE-1 (induced by source paper "inductive representation learning on temporal graphs"):
  with members:
    - "product knowledge graph embedding for e commerce"
    - "generative graph convolutional network for growing graphs"
    - "fast graph representation learning with pytorch geometric"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "dyngem deep embedding method for dynamic graphs"
    - "relational inductive biases deep learning and graph networks"
    - "streaming graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "graph ordering towards the optimal by learning"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "verse versatile graph embeddings from similarity measures"
    - "deep sets"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks"):
  with members:
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-4 (induced by source paper "node embedding over temporal graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dyngem deep embedding method for dynamic graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "graph embedding techniques applications and performance a survey"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "dyngem deep embedding method for dynamic graphs"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "diffusion maps for textual network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "predicting multicellular function through multi layer tissue networks"

Connected through hyperedge HE-6 (induced by source paper "dyngraph2seq dynamic graph to sequence interpretable learning for health stage prediction in online health forums"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "sql to text generation with graph to sequence model"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph r cnn for scene graph generation"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "learning deep generative models of graphs"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic joint variational graph autoencoders"):
  with members:
    - "dynamicgem a library for dynamic graph embedding methods"
    - "dyngem deep embedding method for dynamic graphs"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "learning dynamic embeddings from temporal interactions"
    - "capturing edge attributes via network embedding"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-8 (induced by source paper "evolvegcn evolving graph convolutional networks for dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "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"
    - "attention is all you need"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "neural message passing for quantum chemistry"

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)