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: "the use of machine learning algorithms in recommender systems a systematic review"
  Abstract: Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

Connected through hyperedge HE-1 (induced by source paper "stable and fair classification"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "fair and diverse dpp based data summarization"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "generalization bounds for uniformly stable algorithms"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "certified defenses for data poisoning attacks"

Connected through hyperedge HE-2 (induced by source paper "style conditioned recommendations"):
  with members:
    - "creating capsule wardrobes from fashion images"
    - "be your own prada fashion synthesis with structural coherence"
    - "visually aware fashion recommendation and design with generative image models"
    - "hybrid recommender systems a systematic literature review"
    - "inferring networks of substitutable and complementary products"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "wide deep learning for recommender systems"
    - "deep learning in neural networks"
    - "the continuous cold start problem in e commerce recommender systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "queuing theory guided intelligent traffic scheduling through video analysis using dirichlet process mixture model"):
  with members:
    - "temporal unknown incremental clustering model for analysis of traffic surveillance videos"
    - "queueing analysis of unicast iptv with adaptive modulation and coding in wireless cellular networks"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-5 (induced by source paper "toward a knowledge based personalised recommender system for mobile app development"):
  with members:
    - "evaluation of a recommender system for assisting novice game designers"
    - "ontology based approach for semantic data extraction from social big data state of the art and research directions"

Connected through hyperedge HE-6 (induced by source paper "data driven interaction review of an ed tech application"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-7 (induced by source paper "comparison of recommender systems in an ed tech application"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"

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.SE(Software Engineering)