human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "scalable bayesian preference learning for crowds"):
  Abstract: We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.

Includes members:
  Member "bayesian heatmaps probabilistic classification with multiple unreliable information sources":
    also connected through other hyperedges:
      (none)
  Member "hybrid mst a hybrid active sampling strategy for pairwise preference aggregation":
    also connected through other hyperedges:
      (none)
  Member "finding convincing arguments using scalable bayesian preference learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preference based interactive multi document summarisation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "argument strength is in the eye of the beholder audience effects in persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "distributed bayesian probabilistic matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable bayesian non linear matrix completion")
  Member "robust subjective visual property prediction from crowdsourced pairwise labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing")

Question: Please tell me which class the source paper 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.LG(Machine Learning)