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: "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
  Abstract: With the rapid development of online social media, online shopping sites and cyber-physical systems, heterogeneous information networks have become increasingly popular and content-rich over time. In many cases, such networks contain multiple types of objects and links, as well as different kinds of attributes. The clustering of these objects can provide useful insights in many applications. However, the clustering of such networks can be challenging since (a) the attribute values of objects are often incomplete, which implies that an object may carry only partial attributes or even no attributes to correctly label itself; and (b) the links of different types may carry different kinds of semantic meanings, and it is a difficult task to determine the nature of their relative importance in helping the clustering for a given purpose. In this paper, we address these challenges by proposing a model-based clustering algorithm. We design a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations. The strengths of different types of links are automatically learned, and are determined by the given purpose of clustering. An iterative algorithm is designed for solving the clustering problem, in which the strengths of different types of links and the quality of clustering results mutually enhance each other. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-2 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach"):
  with members:
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "general optimization technique for high quality community detection in complex networks"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "social structure of facebook networks"

Connected through hyperedge HE-3 (induced by source paper "scalable heterogeneous social network alignment through synergistic graph partition"):
  with members:
    - "meta diagram based active social networks alignment"
    - "deepwalk online learning of social representations"
    - "clustering and community detection in directed networks a survey"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "mutual clustering on comparative texts via heterogeneous information networks"):
  with members:
    - "a survey of heterogeneous information network analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "social network fusion and mining a survey"):
  with members:
    - "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "bicycle sharing system analysis and trip prediction"
    - "node2vec scalable feature learning for networks"
    - "organizational chart inference"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "higher order clustering in networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "recurrent meta structure for robust similarity measure in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-7 (induced by source paper "a semantic rich similarity measure in heterogeneous information networks"):
  with members:
    - "forward backward similarity search in knowledge networks"
    - "a survey of heterogeneous information network analysis"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-8 (induced by source paper "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"):
  with members:
    - "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "organizational chart inference"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "defining and evaluating network communities based on ground truth"

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)