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: "stratified transfer learning for cross domain activity recognition"
  Abstract: In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.

Connected through hyperedge HE-1 (induced by source paper "impact of imagenet model selection on domain adaptation"):
  with members:
    - "modified distribution alignment for domain adaptation with pre trained inception resnet"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "automated identification of hookahs waterpipes on instagram an application in feature extraction using convolutional neural network and support vector machine classification"
    - "balanced distribution adaptation for transfer learning"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive survey on transfer learning"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "transfer learning toolkit primers and benchmarks"
    - "domain agnostic learning with disentangled representations"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transfer learning with dynamic adversarial adaptation network"
    - "domain symmetric networks for adversarial domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-3 (induced by source paper "accelerating deep unsupervised domain adaptation with transfer channel pruning"):
  with members:
    - "finding answers from the word of god domain adaptation for neural networks in biblical question answering"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "a novel channel pruning method for deep neural network compression"
    - "balanced distribution adaptation for transfer learning"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (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"
    - "cross position activity recognition with stratified transfer learning"
    - "multi adversarial domain adaptation"
    - "balanced distribution adaptation for transfer learning"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "easy transfer learning by exploiting intra domain structures"):
  with members:
    - "taking human out of learning applications a survey on automated machine learning"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "balanced distribution adaptation for transfer learning"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "conditional adversarial domain adaptation"
    - "joint geometrical and statistical alignment for visual 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)