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: "zero shot learning for code education rubric sampling with deep learning inference"
  Abstract: In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.

Connected through hyperedge HE-1 (induced by source paper "neural cognitive diagnosis for intelligent education systems"):
  with members:
    - "hierarchical context enabled recurrent neural network for recommendation"
    - "ekt exercise aware knowledge tracing for student performance prediction"
    - "transcribing content from structural images with spotlight mechanism"
    - "character level convolutional networks for text classification"
    - "engaging with massive online courses"

Connected through hyperedge HE-2 (induced by source paper "generative grading neural approximate parsing for automated student feedback"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reliable deep grade prediction with uncertainty estimation"
    - "automated essay scoring based on two stage learning"
    - "training classifiers with natural language explanations"
    - "multimodal generative models for scalable weakly supervised learning"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "inference compilation and universal probabilistic programming"

Connected through hyperedge HE-3 (induced by source paper "interpretable cognitive diagnosis with neural network"):
  with members:
    - "hierarchical context enabled recurrent neural network for recommendation"
    - "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"
    - "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.LG(Machine Learning)