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: "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
  Abstract: Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks (e.g., Recurrent Neural Network) to encode users' historical interactions from left to right into hidden representations for making recommendations. Although these methods achieve satisfactory results, they often assume a rigidly ordered sequence which is not always practical. We argue that such left-to-right unidirectional architectures restrict the power of the historical sequence representations. For this purpose, we introduce a Bidirectional Encoder Representations from Transformers for sequential Recommendation (BERT4Rec). However, jointly conditioning on both left and right context in deep bidirectional model would make the training become trivial since each item can indirectly "see the target item". To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context. Comparing with predicting the next item at each position in a sequence, the Cloze task can produce more samples to train a more powerful bidirectional model. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.

Connected through hyperedge HE-1 (induced by source paper "future data helps training modeling future contexts for session based recommendation"):
  with members:
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation"):
  with members:
    - "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"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "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-3 (induced by source paper "beyond clicks modeling multi relational item graph for session based target behavior prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "attention is all you need"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-4 (induced by source paper "learning to structure long term dependence for sequential recommendation"):
  with members:
    - "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"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "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-5 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-6 (induced by source paper "pog personalized outfit generation for fashion recommendation at alibaba ifashion"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "deep contextualized word representations"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "neural compatibility modeling with attentive knowledge distillation"

Connected through hyperedge HE-7 (induced by source paper "superbloom bloom filter meets transformer"):
  with members:
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "next item recommendation with self attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hash embeddings for efficient word representations"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-8 (induced by source paper "conversion rate prediction via post click behaviour modeling"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "sdm sequential deep matching model for online large scale recommender system"
    - "deep session interest network for click through rate prediction"
    - "deep forest"
    - "rank and rate multi task learning for recommender systems"
    - "optimized cost per click in taobao display advertising"
    - "deep forest towards an alternative to deep neural networks"
    - "click through rate prediction for contextual advertisment using linear regression"

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)