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: "evaluation of session based recommendation algorithms"
  Abstract: Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user’s immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec, factorized Markov model approaches such as fism or fossil, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today’s more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "multi pointer co attention networks for recommendation"
    - "neural collaborative ranking"

Connected through hyperedge HE-2 (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"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural attentive session based recommendation"

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 "contextual hybrid session based news recommendation with recurrent neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "news session based recommendations using deep neural networks"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "balancing multi level interactions for session based recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "fast graph representation learning with pytorch geometric"
    - "neural message passing for quantum chemistry"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "are we really making much progress a worrying analysis of recent neural recommendation approaches"):
  with members:
    - "sequence aware recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "attentive neural architecture incorporating song features for music recommendation"
    - "collaborative memory network for recommendation systems"
    - "multi pointer co attention networks for recommendation"
    - "spectral collaborative filtering"
    - "deep reinforcement learning that matters"
    - "neural collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "sequential modeling of sessions using recurrent neural networks for skip prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "the music streaming sessions dataset"
    - "sequence aware recommender systems"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "what makes imagenet good for transfer learning"
    - "session based recommendations with recurrent neural networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "measuring the business value of recommender systems"):
  with members:
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "beyond personalization research directions in multistakeholder recommendation"
    - "sequence aware recommender systems"
    - "price and profit awareness in recommender systems"
    - "unbiased learning to rank with biased feedback"
    - "where to go on your next trip optimizing travel destinations based on user preferences"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "machine learning that matters"

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)