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: "engaging with massive online courses"
  Abstract: The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. #R##N#In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. #R##N#We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.

Connected through hyperedge HE-1 (induced by source paper "domain adaption for knowledge tracing"):
  with members:
    - "enhancing item response theory for cognitive diagnosis"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "addressing two problems in deep knowledge tracing via prediction consistent regularization"
    - "how deep is knowledge tracing"
    - "deep transfer learning with joint adaptation networks"
    - "dynamic key value memory networks for knowledge tracing"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (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"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "the goal gradient hypothesis in stack overflow"):
  with members:
    - "quizz targeted crowdsourcing with a billion potential users"

Connected through hyperedge HE-4 (induced by source paper "ekt exercise aware knowledge tracing for student performance prediction"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "stable prediction across unknown environments"
    - "a survey on network embedding"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "dynamic memory networks for visual and textual question answering"
    - "dynamic key value memory networks for knowledge tracing"
    - "end to end memory networks"
    - "lstm based deep learning models for non factoid answer selection"

Connected through hyperedge HE-5 (induced by source paper "quesnet a unified representation for heterogeneous test questions"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "dynamic key value memory networks for knowledge tracing"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-6 (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"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-7 (induced by source paper "i ll be back on the multiple lives of users of a mobile activity tracking application"):
  with members:
    - "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "wearable computing for health and fitness exploring the relationship between data and human behaviour"

Connected through hyperedge HE-8 (induced by source paper "continuous time user modeling in the presence of badges a probabilistic approach"):
  with members:
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "stochastic models predict user behavior in social media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "using stochastic models to describe and predict social dynamics of web users"
    - "on prediction using variable order markov models"

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.SI(Social and Information Networks)