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: "graph summarization methods and applications a survey"
  Abstract: While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "estimating aggregate properties in relational networks with unobserved data"):
  with members:
    - "gmnn graph markov neural networks"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-3 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-4 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "network embedding on compression and learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a tutorial on network embeddings"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "atp directed graph embedding with asymmetric transitivity preservation"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-6 (induced by source paper "latent network summarization bridging network embedding and summarization"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "a spectral framework for anomalous subgraph detection"

Connected through hyperedge HE-7 (induced by source paper "scalable probabilistic matrix factorization with graph based priors"):
  with members:
    - "convolutional geometric matrix completion"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "graph learning from data under laplacian and structural constraints"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning laplacian matrix in smooth graph signal representations"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "entity summarization state of the art and future challenges"):
  with members:
    - "eventkg the hub of event knowledge on the web and biographical timeline generation"
    - "neural wikipedian generating textual summaries from knowledge base triples"
    - "learning to generate one sentence biographies from wikidata"
    - "neural text generation from structured data with application to the biography domain"
    - "timemachine timeline generation for knowledge base entities"
    - "leveraging usage data for linked data movie entity summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

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.IR(Information Retrieval)