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 "cross position activity recognition with stratified transfer learning"):
  Abstract: Human activity recognition aims to recognize the activities of daily living by utilizing the sensors on different body parts. However, when the labeled data from a certain body position (i.e. target domain) is missing, how to leverage the data from other positions (i.e. source domain) to help learn the activity labels of this position? When there are several source domains available, it is often difficult to select the most similar source domain to the target domain. With the selected source domain, we need to perform accurate knowledge transfer between domains. Existing methods only learn the global distance between domains while ignoring the local property. In this paper, we propose a \textit{Stratified Transfer Learning} (STL) framework to perform both source domain selection and knowledge transfer. STL is based on our proposed \textit{Stratified} distance to capture the local property of domains. STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive experiments on three public activity recognition datasets demonstrate the superiority of STL. Furthermore, we extensively investigate the performance of transfer learning across different degrees of similarities and activity levels between domains. We also discuss the potential applications of STL in other fields of pervasive computing for future research.

Includes members:
  Member "visual domain adaptation with manifold embedded distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "cactusnets layer applicability as a metric for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context aware design of cyber physical human systems cphs")
  Member "balanced distribution adaptation for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "learning to compare relation network for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "joint geometrical and statistical alignment for visual domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive few shot learning")
  Member "a general model for mac protocol selection in wireless sensor networks":
    also connected through other hyperedges:
      (none)
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provably efficient exploration for rl with unsupervised learning")

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.CV(Computer Vision and Pattern Recognition)