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: "atrank an attention based user behavior modeling framework for recommendation"
  Abstract: A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.

Connected through hyperedge HE-1 (induced by source paper "attentive item2vec neural attentive user representations"):
  with members:
    - "neural attentive multiview machines"
    - "scalable attentive sentence pair modeling via distilled sentence embedding"
    - "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-2 (induced by source paper "deep time stream framework for click through rate prediction by tracking interest evolution"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "wide deep learning for recommender systems"
    - "product based neural networks for user response prediction"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-3 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning disentangled representations for recommendation"):
  with members:
    - "disentangling factors of variation using few labels"
    - "visual object networks image generation with disentangled 3d representation"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to decompose and disentangle representations for video prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "gated group self attention for answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "self attentional acoustic models"
    - "non local neural networks"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "deep semantic role labeling with self attention"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-7 (induced by source paper "a domain generalization perspective on listwise context modeling"):
  with members:
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "generalizing across domains via cross gradient training"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-8 (induced by source paper "consistency aware recommendation for user generated itemlist continuation"):
  with members:
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "a hierarchical self attentive model for recommending user generated item lists"
    - "an analysis of approaches taken in the acm recsys challenge 2018 for automatic music playlist continuation"
    - "collaborative memory network for recommendation systems"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "nais neural attentive item similarity model for recommendation"
    - "self attentive sequential recommendation"
    - "attention is all you need"

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.AI(Artificial Intelligence)