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: "meta graph based hin spectral embedding methods analyses and insights"
  Abstract: In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with $\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.

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"
    - "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"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"

Connected through hyperedge HE-2 (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"
    - "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"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"

Connected through hyperedge HE-3 (induced by source paper "neural embedding propagation on heterogeneous networks"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "higitclass keyword driven hierarchical classification of github repositories"):
  with members:
    - "categorizing the content of github readme files"
    - "universal language model fine tuning for text classification"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-5 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "the spacey random walk a stochastic process for higher order data"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"

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"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
    - "automated phrase mining from massive text corpora"

Connected through hyperedge HE-7 (induced by source paper "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"):
  with members:
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "quadratic decomposable submodular function minimization"
    - "submodularity on hypergraphs from sets to sequences"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-8 (induced by source paper "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"):
  with members:
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "higher order spectral clustering for heterogeneous graphs"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "tensor spectral clustering for partitioning higher order network structures"

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.LG(Machine Learning)