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: "a survey on session based recommender systems"
  Abstract: Session-based recommender systems (SBRS) are an emerging topic in the recommendation domain and have attracted much attention from both academia and industry in recent years. Most of existing works only work on modelling the general item-level dependency for recommendation tasks. However, there are many more other challenges at different levels, e.g., item feature level and session level, and from various perspectives, e.g., item heterogeneity and intra- and inter-item feature coupling relations, associated with SBRS. In this paper, we provide a systematic and comprehensive review on SBRS and create a hierarchical and in-depth understanding of a variety of challenges in SBRS. To be specific, we first illustrate the value and significance of SBRS, followed by a hierarchical framework to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities. Further, a summary together with a detailed introduction of the research progress is provided. Lastly, we share some prospects in this research area.

Connected through hyperedge HE-1 (induced by source paper "session based recommender systems for action selection in gui test generation"):
  with members:
    - "a deep learning based approach to automated android app testing"
    - "sequence aware recommender systems"
    - "machine learning and evolutionary computing for gui based regression testing"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "generating realistic synthetic population datasets"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (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"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-3 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "multiplayer bandits without observing collision information"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"

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"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "adversarial personalized ranking for recommendation"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "rethinking the item order in session based recommendation with graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "from zero shot learning to cold start recommendation"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "session aware information embedding for e commerce product recommendation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "deep personalized re targeting"):
  with members:
    - "feature generation by convolutional neural network for click through rate prediction"
    - "large scale collaborative filtering with product embeddings"
    - "an attentive survey of attention models"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "deep neural network marketplace recommenders in online experiments"
    - "deep neural net with attention for multi channel multi touch attribution"
    - "a hierarchical bayesian model for size recommendation in fashion"
    - "word2vec applied to recommendation hyperparameters matter"

Connected through hyperedge HE-7 (induced by source paper "sequential recommender systems challenges progress and prospects"):
  with members:
    - "session based recommendation with graph neural network"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "recommendation through mixtures of heterogeneous item relationships"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "general factorization framework for context aware recommendations"

Connected through hyperedge HE-8 (induced by source paper "a simple deep personalized recommendation system"):
  with members:
    - "session based recommendation with graph neural network"
    - "large scale collaborative filtering with product embeddings"
    - "an attentive survey of attention models"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "deep neural network marketplace recommenders in online experiments"
    - "word2vec applied to recommendation hyperparameters matter"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of words and phrases and their compositionality"

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)