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: "fast als based tensor factorization for context aware recommendation from implicit feedback"
  Abstract: Albeit the implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and "implicitized" Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.

Connected through hyperedge HE-1 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks"):
  with members:
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "gaussian process latent variable model factorization for context aware recommender systems"):
  with members:
    - "gaussian processes for big data"

Connected through hyperedge HE-3 (induced by source paper "learning dynamic embeddings from temporal interactions"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "predicting interactions between individuals with structural and dynamical information"

Connected through hyperedge HE-4 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"

Connected through hyperedge HE-5 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "multi view machines"

Connected through hyperedge HE-6 (induced by source paper "recurrent neural networks with top k gains for session based recommendations"):
  with members:
    - "recurrent latent variable networks for session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "collaborative filtering with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "fairness aware recommendation of information curators"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "beyond parity fairness objectives for collaborative filtering"
    - "new fairness metrics for recommendation that embrace differences"
    - "algorithmic decision making and the cost of fairness"
    - "fa ir a fair top k ranking algorithm"
    - "equality of opportunity in supervised learning"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "expert recommendation via tensor factorization with regularizing hierarchical topical relationships"):
  with members:
    - "outer product based neural collaborative filtering"
    - "a survey on expert recommendation in community question answering"
    - "bpr bayesian personalized ranking from implicit feedback"

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.LG(Machine Learning)