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 "modeling the past and future contexts for session based recommendation"):
  Abstract: Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, previous sequential recommendation models resort either to data augmentation techniques or a left-to-right style autoregressive training approach. While effective, an obvious drawback is that future user behaviors are always missing during model training. In this paper, we argue that users' future action signals can be exploited to boost the recommendation quality. We present GRec, a simple Gap-filling based encoder-decoder Recommendation framework to generative modelling using both past and future contexts. GfedRec encodes a partially-complete item sequence with blank masks, and autoregressively reconstructs the missing item distributions. In contrast with the typical encoder-decoder paradigm used in the computer vision and NLP domains, GfedRec does not have the data leakage problem when jointly training the encoder and decoder conditioned on the same user action sequence. Experiments on real-word datasets with short-, medium- and long-range user sessions show that GRec largely exceeds the performance of its left-to-right counterparts. Empirical evidence confirms that training sequential recommendation models with future contexts is a promising way to offer better recommendation accuracy.

Includes members:
  Member "deep learning based sequential recommender systems concepts algorithms and evaluations":
    also connected through other hyperedges:
      (none)
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "towards neural mixture recommender for long range dependent user sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "future data helps training modeling future contexts for session based recommendation")
  Member "hierarchical temporal convolutional networks for dynamic recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation")
  Member "cosrec 2d convolutional neural networks for sequential recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "future data helps training modeling future contexts for session based recommendation")
  Member "hierarchical gating networks for sequential recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "a survey on session based recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "session based recommender systems for action selection in gui test generation")
  Member "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")

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)