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 "graphgen a scalable approach to domain agnostic labeled graph generation"):
  Abstract: Graph generative models have been extensively studied in the data mining literature. While traditional techniques are based on generating structures that adhere to a pre-decided distribution, recent techniques have shifted towards learning this distribution directly from the data. While learning-based approaches have imparted significant improvement in quality, some limitations remain to be addressed. First, learning graph distributions introduces additional computational overhead, which limits their scalability to large graph databases. Second, many techniques only learn the structure and do not address the need to also learn node and edge labels, which encode important semantic information and influence the structure itself. Third, existing techniques often incorporate domain-specific rules and lack generalizability. Fourth, the experimentation of existing techniques is not comprehensive enough due to either using weak evaluation metrics or focusing primarily on synthetic or small datasets. In this work, we develop a domain-agnostic technique called GraphGen to overcome all of these limitations. GraphGen converts graphs to sequences using minimum DFS codes. Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information. The complex joint distributions between structure and semantic labels are learned through a novel LSTM architecture. Extensive experiments on million-sized, real graph datasets show GraphGen to be 4 times faster on average than state-of-the-art techniques while being significantly better in quality across a comprehensive set of 11 different metrics. Our code is released at https://github.com/idea-iitd/graphgen.

Includes members:
  Member "deepnc deep generative network completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference for network structure and dynamics from time series data via graph neural network")
  Member "d vae a variational autoencoder for directed acyclic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search")
  Member "labeled graph generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learn to generate time series conditioned graphs with generative adversarial nets")
  Member "molecularrnn generating realistic molecular graphs with optimized properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphaf a flow based autoregressive model for molecular graph generation")
  Member "gram scalable generative models for graphs with graph attention mechanism":
    also connected through other hyperedges:
      (none)
  Member "efficient graph generation with graph recurrent attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image conditioned graph generation for road network extraction")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can graph neural networks count substructures")
  Member "constrained graph variational autoencoders for molecule design":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")

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)