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: "substructure discovery using minimum description length and background knowledge"
  Abstract: The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.

Connected through hyperedge HE-1 (induced by source paper "modeling graphs with vertex replacement grammars"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "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"
    - "node2vec scalable feature learning for networks"
    - "growing graphs from hyperedge replacement graph grammars"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-2 (induced by source paper "controversy rules discovering regions where classifiers dis agree exceptionally"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "distances and isomorphism between networks and the stability of network invariants"):
  with members:
    - "the metric space of networks"
    - "hierarchical quasi clustering methods for asymmetric networks"
    - "spectral descriptors for graph matching"
    - "stable and informative spectral signatures for graph matching"
    - "deltacon a principled massive graph similarity function"
    - "axiomatic construction of hierarchical clustering in asymmetric networks"

Connected through hyperedge HE-4 (induced by source paper "large scale network motif analysis using compression"):
  with members:
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficiently estimating motif statistics of large networks"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-5 (induced by source paper "graphzip dictionary based compression for mining graph streams"):
  with members:
    - "gsketch on query estimation in graph streams"

Connected through hyperedge HE-6 (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-7 (induced by source paper "an empirical comparison of the summarization power of graph clustering methods"):
  with members:
    - "vog summarizing and understanding large graphs"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-8 (induced by source paper "detecting blackholes and volcanoes in directed networks"):
  with members:
    - "community detection using a measure of global influence"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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.AI(Artificial Intelligence)