human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graph convolutional topic model for data streams"):
  Abstract: Learning hidden topics in data streams has been paid a great deal of attention by researchers with a lot of proposed methods, but exploiting prior knowledge in general and a knowledge graph in particular has not been taken into adequate consideration in these methods. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g.Word2vec) is very valuable and useful to help topic models work better, especially on short texts. However, previous work often ignores this resource, or it can only utilize prior knowledge of a vector form in a simple way. In this paper, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance between the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both human graph knowledge(Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than the state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at this https URL.

Includes members:
  Member "graph convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta embeddings based on self attention")
  Member "memory aware synapses learning what not to forget":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "bayesian models of data streams with hierarchical power priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic transformation of prior knowledge intobayesian models for data streams")
  Member "continual learning through synaptic intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental object detection via meta learning")
  Member "graph convolutional encoders for syntax aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "overcoming catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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)