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: "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.

Connected through hyperedge HE-1 (induced by source paper "transfer learning with dynamic distribution adaptation"):
  with members:
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "decomposition based transfer distance metric learning for image classification"
    - "easy transfer learning by exploiting intra domain structures"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "transfer learning with dynamic adversarial adaptation network"):
  with members:
    - "accelerating deep unsupervised domain adaptation with transfer channel pruning"
    - "easy transfer learning by exploiting intra domain structures"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "co regularized alignment for unsupervised domain adaptation"
    - "multi adversarial domain adaptation"
    - "balanced distribution adaptation for transfer learning"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"

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.CV(Computer Vision and Pattern Recognition)