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 "domain adaption for knowledge tracing"):
  Abstract: With the rapid development of online education system, knowledge tracing which aims at predicting students' knowledge state is becoming a critical and fundamental task in personalized education. Traditionally, existing methods are domain-specified. However, there are a larger number of domains (e.g., subjects, schools) in the real world and the lacking of data in some domains, how to utilize the knowledge and information in other domains to help train a knowledge tracing model for target domains is increasingly important. We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain. (2) how to transfer good performed knowledge tracing model between domains. To this end, in this paper, we propose a novel adaptable framework, namely adaptable knowledge tracing (AKT) to address the DAKT problem. Specifically, for the first aspect, we incorporate the educational characteristics (e.g., slip, guess, question texts) based on the deep knowledge tracing (DKT) to obtain a good performed knowledge tracing model. For the second aspect, we propose and adopt three domain adaptation processes. First, we pre-train an auto-encoder to select useful source instances for target model training. Second, we minimize the domain-specific knowledge state distribution discrepancy under maximum mean discrepancy (MMD) measurement to achieve domain adaptation. Third, we adopt fine-tuning to deal with the problem that the output dimension of source and target domain are different to make the model suitable for target domains. Extensive experimental results on two private datasets and seven public datasets clearly prove the effectiveness of AKT for great knowledge tracing performance and its superior transferable ability.

Includes members:
  Member "enhancing item response theory for cognitive diagnosis":
    also connected through other hyperedges:
      (none)
  Member "deep irt make deep learning based knowledge tracing explainable using item response theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational item response theory fast accurate and expressive")
  Member "domain adaptive faster r cnn for object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "addressing two problems in deep knowledge tracing via prediction consistent regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "r2de a nlp approach to estimating irt parameters of newly generated questions")
  Member "how deep is knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge tracing with sequential key value memory networks")
  Member "deep transfer learning with joint adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "dynamic key value memory networks for knowledge tracing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "bidirectional lstm crf models for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense dilated convolutions merging network for land cover classification")

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)