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: "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
  Abstract: Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-2 (induced by source paper "collective link prediction oriented network embedding with hierarchical graph attention"):
  with members:
    - "social network fusion and mining a survey"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "node2vec scalable feature learning for networks"
    - "gram graph based attention model for healthcare representation learning"
    - "a convolutional encoder model for neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "relation structure aware heterogeneous information network embedding"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "a survey of heterogeneous information network analysis"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-4 (induced by source paper "ahine adaptive heterogeneous information network embedding"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "regal representation learning based graph alignment"
    - "struc2vec learning node representations from structural identity"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "xgboost a scalable tree boosting system"
    - "node2vec scalable feature learning for networks"

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"
    - "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"
    - "exploiting social network structure for person to person sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "latte application oriented social network embedding"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "deepwalk online learning of social representations"
    - "clustering and community detection in directed networks a survey"
    - "distributed representations of words and phrases and their compositionality"
    - "a tutorial on spectral clustering"

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)