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: "towards neural mixture recommender for long range dependent user sequences"
  Abstract: Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application contexts require adapting to diverse behaviors.    In this paper we examine how to build a model that can make use of different temporal ranges and dynamics depending on the request context. We begin with the analysis of an anonymized Youtube dataset comprising millions of user sequences. We quantify the degree of long-range dependence in these sequences and demonstrate that both short-term and long-term dependent behavioral patterns co-exist. We then propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution to deal with both short-term and long-term dependencies. Our approach employs a mixture of models, each with a different temporal range. These models are combined by a learned gating mechanism capable of exerting different model combinations given different contextual information. In empirical evaluations on a public dataset and our own anonymized YouTube dataset, M3 consistently outperforms state-of-the-art sequential recommendation methods.

Connected through hyperedge HE-1 (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"
    - "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:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "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 "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"
    - "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-4 (induced by source paper "modeling the past and future contexts for session based recommendation"):
  with members:
    - "deep learning based sequential recommender systems concepts algorithms and evaluations"
    - "mass masked sequence to sequence pre training for language generation"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "a survey on session based recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "quantifying long range dependence in language and user behavior to improve rnns"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "attention is all you need"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "sdm sequential deep matching model for online large scale recommender system"):
  with members:
    - "a long short demands aware model for next item recommendation"
    - "deep session interest network for click through rate prediction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
    - "next item recommendation with self attention"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-7 (induced by source paper "time weighted attentional session aware recommender system"):
  with members:
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "sequence aware recommender systems"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "weakly supervised hierarchical text classification"
    - "mining entity synonyms with efficient neural set generation"
    - "session aware information embedding for e commerce product recommendation"
    - "improving session recommendation with recurrent neural networks by exploiting dwell time"

Connected through hyperedge HE-8 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "a dual framework for low rank tensor completion"
    - "structural deep embedding for hyper networks"
    - "efficient softmax approximation for gpus"
    - "a neural autoregressive approach to collaborative filtering"

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.LG(Machine Learning)