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: "vog summarizing and understanding large graphs"
  Abstract: How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a "vocabulary" of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a well-founded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. #R##N#Our contributions are three-fold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop \method, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multi-million-edge real graphs, including Flickr and the Notre Dame web graph.

Connected through hyperedge HE-1 (induced by source paper "towards interpretable graph modeling with vertex replacement grammars"):
  with members:
    - "edge replacement grammars a formal language approach for generating graphs"
    - "modeling graphs with vertex replacement grammars"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "motifs in temporal networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations"):
  with members:
    - "grammar based graph compression"
    - "compressing networks with super nodes"
    - "graphzip dictionary based compression for mining graph streams"
    - "parallel construction of compact planar embeddings"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "fully dynamic de bruijn graphs"
    - "compressing graphs and indexes with recursive graph bisection"

Connected through hyperedge HE-3 (induced by source paper "large graph exploration via subgraph discovery and decomposition"):
  with members:
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "vqa visual question answering"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "compressing graphs and indexes with recursive graph bisection"
    - "network structure inference a survey motivations methods and applications"
    - "efficient frequent directions algorithm for sparse matrices"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-5 (induced by source paper "hyperbolae are no hyperbole modelling communities that are not cliques"):
  with members:
    - "what you will gain by rounding theory and algorithms for rounding rank"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "edgecentric anomaly detection in edge attributed networks"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "birdnest bayesian inference for ratings fraud detection"
    - "spotting suspicious link behavior with fbox an adversarial perspective"

Connected through hyperedge HE-7 (induced by source paper "an empirical comparison of the summarization power of graph clustering methods"):
  with members:
    - "empirical comparison of algorithms for network community detection"
    - "substructure discovery using minimum description length and background knowledge"

Connected through hyperedge HE-8 (induced by source paper "linearized and single pass belief propagation"):
  with members:
    - "semi supervised learning with heterophily"
    - "linview incremental view maintenance for complex analytical queries"
    - "deltacon a principled massive graph similarity function"
    - "spectral redemption in clustering sparse networks"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "paris probabilistic alignment of relations instances and schema"
    - "data conflict resolution using trust mappings"
    - "believe it or not adding belief annotations to databases"

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.SI(Social and Information Networks)