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: "social knowledge based recommender system application to the movies domain"
  Abstract: With the advent of the Social Web and the growing popularity of Web 2.0 applications, recommender systems are gaining momentum. The recommendations generated by these systems aim to provide end users with suggestions about information items, social elements, products or services that are likely to be of their interest. The traditional syntactic-based recommender systems suffer from a number of shortcomings that hamper their effectiveness. As semantic technologies mature, they provide a consistent and reliable basis for dealing with data at the knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a hybrid recommender system based on knowledge and social networks is presented. Its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.

Connected through hyperedge HE-1 (induced by source paper "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"):
  with members:
    - "on multi relational link prediction with bilinear models"
    - "translation based recommendation"
    - "convolutional 2d knowledge graph embeddings"
    - "on complex valued convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "transa an adaptive approach for knowledge graph embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "clustered monotone transforms for rating factorization"):
  with members:
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "neural network matrix factorization"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "learning to rank with bregman divergences and monotone retargeting"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-3 (induced by source paper "a review on recommendation systems context aware to social based"):
  with members:
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-4 (induced by source paper "a hybrid recommendation method based on feature for offline book personalization"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "ultra accurate collaborative information filtering via directed user similarity"):
  with members:
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-6 (induced by source paper "membership in social networks and the application in information filtering"):
  with members:
    - "finding missing edges in networks based on their community structure"
    - "defining and evaluating network communities based on ground truth"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-7 (induced by source paper "tag aware recommender systems a state of the art survey"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "empirical comparison of algorithms for network community detection"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "collective dynamics of social annotation"
    - "adaptive model for recommendation of news"
    - "collaborative tagging as a tripartite network"

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.IR(Information Retrieval)