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: "connectionist recommendation in the wild on the utility and scrutability of neural networks for personalized course guidance"
  Abstract: The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.

Connected through hyperedge HE-1 (induced by source paper "will this course increase or decrease your gpa towards grade aware course recommendation"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "rank aggregation for course sequence discovery"
    - "personalized course sequence recommendations"
    - "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 "combating the filter bubble designing for serendipity in a university course recommendation system"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "attention is all you need"
    - "matching networks for one shot learning"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-3 (induced by source paper "goal based course recommendation"):
  with members:
    - "student success prediction in moocs"
    - "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.AI(Artificial Intelligence)