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: "hierarchical context enabled recurrent neural network for recommendation"
  Abstract: A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our \textit{interest drift assumption}. As we suggest a new RNN structure, we support HCRNN with a complementary \textit{bi-channel attention} structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.

Connected through hyperedge HE-1 (induced by source paper "neural cognitive diagnosis for intelligent education systems"):
  with members:
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "transcribing content from structural images with spotlight mechanism"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "character level convolutional networks for text classification"
    - "engaging with massive online courses"

Connected through hyperedge HE-2 (induced by source paper "sequential recommendation with relation aware kernelized self attention"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "context aware self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-3 (induced by source paper "interpretable cognitive diagnosis with neural network"):
  with members:
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "navigating with graph representations for fast and scalable decoding of neural language models"
    - "transcribing content from structural images with spotlight mechanism"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "unsupervised image to image translation networks"
    - "engaging with massive online courses"

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)