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: "general factorization framework for context aware recommendations"
  Abstract: Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework's potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments--performed on five real life, implicit feedback datasets--that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.

Connected through hyperedge HE-1 (induced by source paper "a survey on session based recommender systems"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "session aware information embedding for e commerce product recommendation"
    - "leveraging long and short term information in content aware movie recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "leverage implicit feedback for context aware product search"):
  with members:
    - "sequence aware recommender systems"
    - "learning a deep listwise context model for ranking refinement"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "session aware information embedding for e commerce product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "on application of learning to rank for e commerce search"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-3 (induced by source paper "sequential recommender systems challenges progress and prospects"):
  with members:
    - "session based recommendation with graph neural network"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "a survey on session based recommender systems"
    - "recommendation through mixtures of heterogeneous item relationships"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"

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"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"
    - "massively parallel methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "contextual sequence modeling for recommendation with recurrent neural networks"):
  with members:
    - "syntax aware neural machine translation using ccg"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "architectural complexity measures of recurrent neural networks"
    - "linguistic input features improve neural machine translation"
    - "on multiplicative integration with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-6 (induced by source paper "deep coevolutionary network embedding user and item features for recommendation"):
  with members:
    - "improved recurrent neural networks for session based recommendations"
    - "discriminative embeddings of latent variable models for structured data"
    - "session based recommendations with recurrent neural networks"
    - "dynamic poisson factorization"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-7 (induced by source paper "tensor methods and recommender systems"):
  with members:
    - "context aware recommendations from implicit data via scalable tensor factorization"
    - "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.IR(Information Retrieval)