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: "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
  Abstract: Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they overlook the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.

Connected through hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "relation learning on social networks with multi modal graph edge variational autoencoders"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "place deduplication with embeddings"
    - "query specific knowledge summarization with entity evolutionary networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"

Connected through hyperedge HE-2 (induced by source paper "disentangling overlapping beliefs by structured matrix factorization"):
  with members:
    - "users polarization on facebook and youtube"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "scoring lexical entailment with a supervised directional similarity network"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "term definitions help hypernymy detection"

Connected through hyperedge HE-4 (induced by source paper "is a single vector enough exploring node polysemy for network embedding"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "techniques for interpretable machine learning"
    - "attributed social network embedding"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"):
  with members:
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "hyperbolic image embeddings"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "inriasac simple hypernym extraction methods"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "automated phrase mining from massive text corpora"

Connected through hyperedge HE-7 (induced by source paper "business taxonomy construction using concept level hierarchical clustering"):
  with members:
    - "partout a distributed engine for efficient rdf processing"

Connected through hyperedge HE-8 (induced by source paper "mapping the underground towards automatic discovery of cybercrime supply chains"):
  with members:
    - "identifying products in online cybercrime marketplaces a dataset for fine grained domain adaptation"
    - "xgboost a scalable tree boosting system"
    - "stress testing the booters understanding and undermining the business of ddos services"

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.DB(Databases)