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 "neural hybrid recommender recommendation needs collaboration"):
  Abstract: In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks. This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources. We have worked on item prediction problems, but the framework can be used for rating prediction problems as well with a single change on the loss function. To evaluate the effect of such a framework, we have tested our approach on benchmark and not yet experimented datasets. The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art methods.

Includes members:
  Member "autoint automatic feature interaction learning via self attentive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a sparse deep factorization machine for efficient ctr prediction")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "deep cross network for ad click predictions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "fast matrix factorization for online recommendation with implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "deepfm a factorization machine based neural network for ctr prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "fine grained analysis of sentence embeddings using auxiliary prediction tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "wide deep learning for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "a generic coordinate descent framework for learning from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")

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.IR(Information Retrieval)