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: "semantic word clouds with background corpus normalization and t distributed stochastic neighbor embedding"
  Abstract: Many word clouds provide no semantics to the word placement, but use a random layout optimized solely for aesthetic purposes. We propose a novel approach to model word significance and word affinity within a document, and in comparison to a large background corpus. We demonstrate its usefulness for generating more meaningful word clouds as a visual summary of a given document. We then select keywords based on their significance and construct the word cloud based on the derived affinity. Based on a modified t-distributed stochastic neighbor embedding (t-SNE), we generate a semantic word placement. For words that cooccur significantly, we include edges, and cluster the words according to their cooccurrence. For this we designed a scalable and memory-efficient sketch-based approach usable on commodity hardware to aggregate the required corpus statistics needed for normalization, and for identifying keywords as well as significant cooccurences. We empirically validate our approch using a large Wikipedia corpus.

Connected through hyperedge HE-1 (induced by source paper "from free text to clusters of content in health records an unsupervised graph partitioning approach"):
  with members:
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "the many facets of community detection in complex networks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "extracting information from free text through unsupervised graph based clustering an application to patient incident records"):
  with members:
    - "from free text to clusters of content in health records an unsupervised graph partitioning approach"
    - "the many facets of community detection in complex networks"
    - "distributed representations of sentences and documents"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-3 (induced by source paper "elki a large open source library for data analysis elki release 0 7 5 heidelberg"):
  with members:
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "an experimental exploration of marsaglia s xorshift generators scrambled"
    - "a new local distance based outlier detection approach for scattered real world data"
    - "objective criteria for the evaluation of clustering methods"

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)