human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "personalized top n sequential recommendation via convolutional sequence embedding"
  Abstract: Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation"):
  with members:
    - "future data helps training modeling future contexts for session based recommendation"
    - "parameter efficient transfer learning for nlp"
    - "albert a lite bert for self supervised learning of language representations"
    - "darec deep domain adaptation for cross domain recommendation via transferring rating patterns"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "vl bert pre training of generic visual linguistic representations"
    - "do better imagenet models transfer better"
    - "conet collaborative cross networks for cross domain recommendation"

Connected through hyperedge HE-3 (induced by source paper "future data helps training modeling future contexts for session based recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "mass masked sequence to sequence pre training for language generation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "deep contextualized word representations"
    - "next item recommendation with self attention"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-4 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-5 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "time is of the essence a joint hierarchical rnn and point process model for time and item predictions"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-6 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling"):
  with members:
    - "lifelong sequential modeling with personalized memorization for user response prediction"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"

Connected through hyperedge HE-7 (induced by source paper "learning to structure long term dependence for sequential recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "bridging the gap between training and inference for neural machine translation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "quantifying long range dependence in language and user behavior to improve rnns"
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"

Connected through hyperedge HE-8 (induced by source paper "ham hybrid associations model with pooling for sequential recommendation"):
  with members:
    - "is attention interpretable"
    - "attention is not explanation"
    - "hierarchical gating networks for sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "self attentive sequential recommendation"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "session based recommendations with recurrent neural networks"

Question: Please tell me which class the center node 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)