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: "self attentive sequential recommendation"
  Abstract: Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently. To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Markov Chains assume that a user's next action can be predicted on the basis of just their last (or last few) actions, while RNNs in principle allow for longer-term semantics to be uncovered. Generally speaking, MC-based methods perform best in extremely sparse datasets, where model parsimony is critical, while RNNs perform better in denser datasets where higher model complexity is affordable. The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC). At each time step, SASRec seeks to identify which items are `relevant' from a user's action history, and use them to predict the next item. Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets. Moreover, the model is an order of magnitude more efficient than comparable CNN/RNN-based models. Visualizations on attention weights also show how our model adaptively handles datasets with various density, and uncovers meaningful patterns in activity sequences.

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 "robustness verification for transformers"):
  with members:
    - "correctness verification of neural networks"
    - "adversarial examples with difficult common words for paraphrase identification"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "verification of non linear specifications for neural networks"

Connected through hyperedge HE-3 (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-4 (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-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"
    - "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"

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 "privileged features distillation at taobao recommendations"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-8 (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"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "attention is all you need"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"

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)