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: "challenging the long tail recommendation"
  Abstract: The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been largely attributed to a "long tail" phenomenon. Although the majority of their inventory is not in high demand, these niche products, unavailable at limited-inventory competitors, generate a significant fraction of total revenue in aggregate. In addition, tail product availability can boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche tastes. However, most of existing recommender systems, especially collaborative filter based methods, can not recommend tail products due to the data sparsity issue. It has been widely acknowledged that to recommend popular products is easier yet more trivial while to recommend long tail products adds more novelty yet it is also a more challenging task. In this paper, we propose a novel suite of graph-based algorithms for the long tail recommendation. We first represent user-item information with undirected edge-weighted graph and investigate the theoretical foundation of applying Hitting Time algorithm for long tail item recommendation. To improve recommendation diversity and accuracy, we extend Hitting Time and propose efficient Absorbing Time algorithm to help users find their favorite long tail items. Finally, we refine the Absorbing Time algorithm and propose two entropy-biased Absorbing Cost algorithms to distinguish the variation on different user-item rating pairs, which further enhances the effectiveness of long tail recommendation. Empirical experiments on two real life datasets show that our proposed algorithms are effective to recommend long tail items and outperform state-of-the-art recommendation techniques.

Connected through hyperedge HE-1 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "a dual framework for low rank tensor completion"
    - "structural deep embedding for hyper networks"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-2 (induced by source paper "scalable realistic recommendation datasets through fractal expansions"):
  with members:
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "a dual framework for low rank tensor completion"
    - "structural deep embedding for hyper networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "scikit image image processing in python"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "eigenrec generalizing puresvd for effective and efficient top n recommendations"):
  with members:
    - "unbiased learning to rank with biased feedback"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "recommendations as treatments debiasing learning and evaluation"
    - "feature based factorized bilinear similarity model for cold start top n item recommendation"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"

Connected through hyperedge HE-4 (induced by source paper "managing popularity bias in recommender systems with personalized re ranking"):
  with members:
    - "beyond personalization research directions in multistakeholder recommendation"
    - "personalizing fairness aware re ranking"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-5 (induced by source paper "reducing popularity bias in recommendation over time"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "personalizing fairness aware re ranking"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-6 (induced by source paper "incorporating system level objectives into recommender systems"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "beyond personalization research directions in multistakeholder recommendation"
    - "a multi objective learning to re rank approach to optimize online marketplaces for multiple stakeholders"

Connected through hyperedge HE-7 (induced by source paper "personalised novel and explainable matrix factorisation"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "a generic top n recommendation framework for trading off accuracy novelty and coverage"):
  with members:
    - "collaborative filtering and the missing at random assumption"
    - "a comparative study of collaborative filtering algorithms"

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.DB(Databases)