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 "attributed multi relational attention network for fact checking url recommendation"):
  Abstract: To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., this http URL and this http URL). However, fake news dissemination has been greatly promoted via social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms eight state-of-the-art recommendation models, achieving at least 3~5.3% improvement.

Includes members:
  Member "learning from fact checkers analysis and generation of fact checking language":
    also connected through other hyperedges:
      (none)
  Member "neural graph collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "the rise of guardians fact checking url recommendation to combat fake news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "collaborative memory network for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "large scale learnable graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "semi supervised content based detection of misinformation via tensor embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "nais neural attentive item similarity model for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")

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)