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: "an o m algorithm for cores decomposition of networks"
  Abstract: The structure of large networks can be revealed by partitioning them to smaller parts, which are easier to handle. One of such decompositions is based on k{cores, proposed in 1983 by Seidman. In the paper an ecien t, O(m), m is the number of lines, algorithm for determining the cores decomposition of a given simple network is presented. An application on the authors collaboration network in computational geometry is presented.

Connected through hyperedge HE-1 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding"):
  with members:
    - "simple and effective graph autoencoders with one hop linear models"
    - "gravity inspired graph autoencoders for directed link prediction"
    - "a degeneracy framework for scalable graph autoencoders"
    - "effective decoding in graph auto encoder using triadic closure"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semi implicit graph variational auto encoders"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "permutation invariant graph generation via score based generative modeling"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "generative modeling by estimating gradients of the data distribution"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph normalizing flows"
    - "exploring randomly wired neural networks for image recognition"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-3 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "generalized two mode cores"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-5 (induced by source paper "pre training graph neural networks for generic structural feature extraction"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "chargrid ocr end to end trainable optical character recognition for printed documents using instance segmentation"):
  with members:
    - "convolutional character networks"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "yolov3 an incremental improvement"
    - "chargrid towards understanding 2d documents"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "aon towards arbitrarily oriented text recognition"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "a degeneracy framework for scalable graph autoencoders"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning to make predictions on graphs with autoencoders"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "recent advances in autoencoder based representation learning"

Connected through hyperedge HE-8 (induced by source paper "efficient graph generation with graph recurrent attention networks"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "exploring randomly wired neural networks for image recognition"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of 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.DS(Data Structures and Algorithms)