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: "organizational chart inference"
  Abstract: Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the "enterprise social networks" (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC (Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.

Connected through hyperedge HE-1 (induced by source paper "structure preserving community in a multilayer network definition detection and analysis"):
  with members:
    - "generic multilayer network data analysis with the fusion of content and structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "querying knowledge graphs by example entity tuples"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-2 (induced by source paper "an efficient framework for computing structure and semantics preserving community in a heterogeneous multilayer network"):
  with members:
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (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"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "exploiting social network structure for person to person sentiment analysis"

Connected through hyperedge HE-4 (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"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"):
  with members:
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"

Connected through hyperedge HE-6 (induced by source paper "a survey of heterogeneous information network analysis"):
  with members:
    - "a survey of rdf data management systems"
    - "predicting neighbor distribution in heterogeneous information networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "integrating heterogeneous information via flexible regularization framework for recommendation"
    - "classification of message spreading in a heterogeneous social network"
    - "scalable topical phrase mining from text corpora"
    - "future influence ranking of scientific literature"
    - "predicting social links for new users across aligned heterogeneous social networks"

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.SI(Social and Information Networks)