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: "extracting implicit social relation for social recommendation techniques in user rating prediction"
  Abstract: Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction.

Connected through hyperedge HE-1 (induced by source paper "generating reliable friends via adversarial training to improve social recommendation"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "graph neural networks for social recommendation"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial personalized ranking for recommendation"
    - "neural collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "compressive closeness in networks"):
  with members:
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "defining and evaluating network communities based on ground truth"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "compressive sensing over graphs"
    - "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 "a matrix factorization model for hellinger based trust management in social internet of things"):
  with members:
    - "clarifying trust in social internet of things"
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "collaborative deep learning for recommender systems"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (induced by source paper "hellrank a hellinger based centrality measure for bipartite social networks"):
  with members:
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "user recommendations in reciprocal and bipartite social networks an online dating case study"
    - "identifying influential nodes in bipartite networks using the clustering coefficient"
    - "graph cluster randomization network exposure to multiple universes"
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network sampling"

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)