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: "integrating heterogeneous information via flexible regularization framework for recommendation"
  Abstract: Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are absent or very sparse. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit these attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impact on users and items.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "neural collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "heterogeneous network approach to predict individuals mental health"):
  with members:
    - "the power of dynamic social networks to predict individuals mental health"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "social network structure is predictive of health and wellness"
    - "can we assess mental health through social media and smart devices addressing bias in methodology and evaluation"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-3 (induced by source paper "multi component graph convolutional collaborative filtering"):
  with members:
    - "spectral collaborative filtering"
    - "semi supervised classification with graph convolutional networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "diffusion convolutional neural networks"
    - "matrix completion on graphs"

Connected through hyperedge HE-4 (induced by source paper "heterogeneous information network embedding for recommendation"):
  with members:
    - "harp hierarchical representation learning for networks"
    - "a survey on network embedding"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "a survey of heterogeneous information network analysis"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-5 (induced by source paper "a survey of heterogeneous information network analysis"):
  with members:
    - "a survey of rdf data management systems"
    - "organizational chart inference"
    - "predicting neighbor distribution in heterogeneous information networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "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)