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: "word2vec applied to recommendation hyperparameters matter"
  Abstract: Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting, with an extensive joint hyperparameter optimization on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it up to a factor of $10$.

Connected through hyperedge HE-1 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "distributional semantic modeling a revised technique to train term word vector space models applying the ontology related approach"):
  with members:
    - "development of formal models algorithms procedures engineering and functioning of the software system instrumental complex for ontological engineering purpose"
    - "deep contextualized word representations"
    - "integrated tools for engineering ontologies"
    - "to the problem of the instrumental complex for ontological engineering purpose software system design"
    - "research and development workstation environment the new class of current research information systems"
    - "technique for designing a domain ontology"
    - "fasttext zip compressing text classification models"
    - "enriching word vectors with subword information"

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"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-4 (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"
    - "a survey on session based recommender systems"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "improving collaborative metric learning with efficient negative sampling"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning spread out local feature descriptors"
    - "how to train triplet networks with 100k identities"
    - "smart mining for deep metric learning"
    - "item2vec neural item embedding for collaborative filtering"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-6 (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"
    - "a survey on session based recommender systems"
    - "an attentive survey of attention models"
    - "predicting purchasing intent automatic feature learning using recurrent neural networks"
    - "deep neural network marketplace recommenders in online experiments"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "understanding stability of medical concept embeddings analysis and prediction"):
  with members:
    - "medical concept embedding with time aware attention"
    - "jointly embedding entities and text with distant supervision"
    - "factors influencing the surprising instability of word embeddings"
    - "corpus specificity in lsa and word2vec the role of out of domain documents"
    - "gram graph based attention model for healthcare representation learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "analyzing hypersensitive ai instability in corporate scale machine learning"):
  with members:
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "sentiment analysis of citations using word2vec"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "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)