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: "distributed bayesian probabilistic matrix factorization"
  Abstract: Abstract   Using the matrix factorization technique in machine learning is very common mainly in areas like rec-ommender systems. Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of the prohibitive cost. In this paper, we propose a distributed high-performance parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures. We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations.

Connected through hyperedge HE-1 (induced by source paper "scalable bayesian preference learning for crowds"):
  with members:
    - "bayesian heatmaps probabilistic classification with multiple unreliable information sources"
    - "hybrid mst a hybrid active sampling strategy for pairwise preference aggregation"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "distributed bayesian probabilistic matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "scalable bayesian non linear matrix completion"):
  with members:
    - "distributed bayesian probabilistic matrix factorization"
    - "gaussian process models with parallelization and gpu acceleration"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"
    - "manifold relevance determination"

Connected through hyperedge HE-3 (induced by source paper "smurff a high performance framework for matrix factorization"):
  with members:
    - "sparse group factor analysis for biclustering of multiple data sources"
    - "distributed bayesian probabilistic matrix factorization"

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.DC(Distributed, Parallel, and Cluster Computing)