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 "parallel split join networks for shared account cross domain sequential recommendations"):
  Abstract: Sequential Recommendation (SR) has been attracting a growing attention for the superiority in modeling sequential information of user behaviors. We study SR in a particularly challenging context, in which multiple individual users share a single account (shared-account) and in which user behaviors are available in multiple domains (cross-domain). These characteristics bring new challenges on top of those of the traditional SR task. On the one hand, we need to identify the behaviors by different user roles under the same account in order to recommend the right item to the right user role at the right time. On the other hand, we need to discriminate the behaviors from one domain that might be helpful to improve recommendations in the other domains. In this work, we formulate Shared-account Cross-domain Sequential Recommendation (SCSR) and propose a parallel modeling network to address the two challenges above, namely Parallel Split-Join Network (PSJNet). We present two variants of PSJNet, PSJNet-I and PSJNet-II. PSJNet-I is a "Split-by-Join" framework where it splits the mixed representations to get role-specific representations and join them to get cross-domain representations at each timestamp simultaneously. PSJNet-II is a "Split-and-Join" framework where it first splits role-specific representations at each timestamp, and then the representations from all timestamps and all roles are joined to get cross-domain representations. We use two datasets to assess the effectiveness of PSJNet. The first dataset is a simulated SCSR dataset obtained by randomly merging the Amazon logs from different users in movie and book domains. The second dataset is a real-world SCSR dataset built from smart TV watching logs of a commercial company. Our experimental results demonstrate that PSJNet outperforms state-of-the-art baselines in terms of MRR and Recall.

Includes members:
  Member "conet collaborative cross networks for cross domain recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "cross domain recommendation via deep domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel segregation integration networks for shared account cross domain sequential recommendations")
  Member "aspect aware latent factor model rating prediction with ratings and reviews":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")
  Member "personalized top n sequential recommendation via convolutional sequence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in collaborative filtering and ranking")
  Member "item silk road recommending items from information domains to social users":
    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 "personalizing session based recommendations with hierarchical recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)