human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural cognitive diagnosis for intelligent education systems"):
  Abstract: Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.

Includes members:
  Member "hierarchical context enabled recurrent neural network for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential recommendation with relation aware kernelized self attention")
  Member "ekt exercise aware knowledge tracing for student performance prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assessment modeling fundamental pre training tasks for interactive educational systems")
  Member "transcribing content from structural images with spotlight mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ekt exercise aware knowledge tracing for student performance prediction")
  Member "zero shot learning for code education rubric sampling with deep learning inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative grading neural approximate parsing for automated student feedback")
  Member "character level convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "engaging with massive online courses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaption for knowledge tracing")

Question: Please tell me which class the source paper 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.LG(Machine Learning)