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: "are we really making much progress a worrying analysis of recent neural recommendation approaches"
  Abstract: Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models.   In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. The remaining one clearly outperformed the baselines but did not consistently outperform a well-tuned non-neural linear ranking method. Overall, our work sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area.

Connected through hyperedge HE-1 (induced by source paper "simple and effective graph autoencoders with one hop linear models"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "effective decoding in graph auto encoder using triadic closure"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"

Connected through hyperedge HE-2 (induced by source paper "post estimation smoothing a simple baseline for learning with side information"):
  with members:
    - "predicting 3d human dynamics from video"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning 3d human dynamics from video"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "end to end recovery of human shape and pose"
    - "learning 3d human pose from structure and motion"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "joint geographical and temporal modeling based on matrix factorization for point of interest recommendation"):
  with members:
    - "a joint two phase time sensitive regularized collaborative ranking model for point of interest recommendation"
    - "lglmf local geographical based logistic matrix factorization model for poi recommendation"
    - "category aware location embedding for point of interest recommendation"
    - "personalized context aware point of interest recommendation"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (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"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-5 (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-6 (induced by source paper "towards amortized ranking critical training for collaborative filtering"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "embarrassingly shallow autoencoders for sparse data"
    - "deep learning based recommender system a survey and new perspectives"
    - "cgans with projection discriminator"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-7 (induced by source paper "dividing and conquering cross modal recipe retrieval from nearest neighbours baselines to sota"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "kitchenette predicting and ranking food ingredient pairings using siamese neural network"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "arcface additive angular margin loss for deep face recognition"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"

Connected through hyperedge HE-8 (induced by source paper "searching for interaction functions in collaborative filtering"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "accelerated and inexact soft impute for large scale matrix and tensor completion"
    - "neural collaborative filtering"

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)