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: "personalized course sequence recommendations"
  Abstract: Given the variability in student learning, it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multiarmed bandits, an algorithm is developed that can optimally recommend a course sequence that both reduces the time to graduate while also increasing the overall GPA of the student. The algorithm dynamically learns how students with different contextual backgrounds perform for given course sequences and, then, recommends an optimal course sequence for new students. Using real-world student data from the UCLA Mechanical and Aerospace Engineering Department, we illustrate how the proposed algorithms outperform other methods that do not include student contextual information when making course sequence recommendations.

Connected through hyperedge HE-1 (induced by source paper "will this course increase or decrease your gpa towards grade aware course recommendation"):
  with members:
    - "connectionist recommendation in the wild on the utility and scrutability of neural networks for personalized course guidance"
    - "node2vec scalable feature learning for networks"
    - "rank aggregation for course sequence discovery"
    - "latent skill embedding for personalized lesson sequence recommendation"
    - "grade prediction with course and student specific models"
    - "line large scale information network embedding"
    - "e commerce in your inbox product recommendations at scale"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "ednet a large scale hierarchical dataset in education"):
  with members:
    - "creating a neural pedagogical agent by jointly learning to review and assess"
    - "exploiting cognitive structure for adaptive learning"
    - "a self attentive model for knowledge tracing"
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "knowledge tracing with sequential key value memory networks"
    - "dynamic key value memory networks for knowledge tracing"

Connected through hyperedge HE-3 (induced by source paper "sequence aware recommender systems"):
  with members:
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "learning distributed representations from reviews for collaborative filtering"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-4 (induced by source paper "multi objective contextual multi armed bandit with a dominant objective"):
  with members:
    - "releaf an algorithm for learning and exploiting relevance"
    - "adaptive ensemble learning with confidence bounds"
    - "distributed online learning in social recommender systems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-5 (induced by source paper "rank aggregation for course sequence discovery"):
  with members:
    - "mapping the curricular structure and contents of network science courses"
    - "sync rank robust ranking constrained ranking and rank aggregation via eigenvector and semidefinite programming synchronization"
    - "methods for ordinal peer grading"
    - "pagerank beyond the web"
    - "rank centrality ranking from pair wise comparisons"
    - "least squares ranking on graphs"

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.CY(Computers and Society)