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: "price and profit awareness in recommender systems"
  Abstract: Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.

Connected through hyperedge HE-1 (induced by source paper "beyond personalization research directions in multistakeholder recommendation"):
  with members:
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "a fairness aware hybrid recommender system"
    - "personalizing fairness aware re ranking"
    - "synthetic attribute data for evaluating consumer side fairness"
    - "an algorithmic framework to control bias in bandit based personalization"
    - "exploring author gender in book rating and recommendation"
    - "beyond parity fairness objectives for collaborative filtering"
    - "multisided fairness for recommendation"

Connected through hyperedge HE-2 (induced by source paper "measuring the business value of recommender systems"):
  with members:
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "beyond personalization research directions in multistakeholder recommendation"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "unbiased learning to rank with biased feedback"
    - "where to go on your next trip optimizing travel destinations based on user preferences"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "machine learning that matters"

Connected through hyperedge HE-3 (induced by source paper "multi stakeholder recommendation and its connection to multi sided fairness"):
  with members:
    - "managing popularity bias in recommender systems with personalized re ranking"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "beyond personalization research directions in multistakeholder recommendation"
    - "beyond parity fairness objectives for collaborative filtering"
    - "reciprocal recommendation system for online dating"

Connected through hyperedge HE-4 (induced by source paper "leveraging multi method evaluation for multi stakeholder settings"):
  with members:
    - "multisided fairness for recommendation"
    - "multiple stakeholders in music recommender systems"

Connected through hyperedge HE-5 (induced by source paper "action conditional sequence modeling for recommendation"):
  with members:
    - "sequence aware recommender systems"
    - "deep reinforcement learning for list wise recommendations"
    - "deconvolving feedback loops in recommender systems"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "recurrent environment simulators"
    - "wide deep learning for recommender systems"
    - "recommendations as treatments debiasing learning and evaluation"
    - "session based recommendations with recurrent neural networks"

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)