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: "on the difficulty of evaluating baselines a study on recommender systems"
  Abstract: Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is difficult. We demonstrate this issue on two extensively studied datasets. First, we show that results for baselines that have been used in numerous publications over the past five years for the Movielens 10M benchmark are suboptimal. With a careful setup of a vanilla matrix factorization baseline, we are not only able to improve upon the reported results for this baseline but even outperform the reported results of any newly proposed method. Secondly, we recap the tremendous effort that was required by the community to obtain high quality results for simple methods on the Netflix Prize. Our results indicate that empirical findings in research papers are questionable unless they were obtained on standardized benchmarks where baselines have been tuned extensively by the research community.

Connected through hyperedge HE-1 (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"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"

Connected through hyperedge HE-2 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "recommendations and user agency the reachability of collaboratively filtered information"):
  with members:
    - "sequential learning over implicit feedback for robust large scale recommender systems"
    - "when collaborative filtering meets reinforcement learning"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "efficient search for diverse coherent explanations"
    - "the social cost of strategic classification"
    - "causal embeddings for recommendation"
    - "exploring author gender in book rating and recommendation"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-4 (induced by source paper "a troubling analysis of reproducibility and progress in recommender systems research"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "embarrassingly shallow autoencoders for sparse data"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "attentive neural architecture incorporating song features for music recommendation"
    - "outer product based neural collaborative filtering"
    - "collaborative memory network for recommendation systems"
    - "spectral collaborative filtering"
    - "meta prod2vec product embeddings using side information for recommendation"

Connected through hyperedge HE-5 (induced by source paper "how robust is movielens a dataset analysis for recommender systems"):
  with members:
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"

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)