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: "recent advances in transfer learning for cross dataset visual recognition a problem oriented perspective"
  Abstract: This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly.

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"
    - "cross position activity recognition with stratified transfer learning"
    - "multi adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "using context information to enhance simple question answering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "kbqa learning question answering over qa corpora and knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "on boosting semantic street scene segmentation with weak supervision"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "learning semantic segmentation with diverse supervision"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "domain adaptation for visual applications a comprehensive survey"
    - "transfer learning for cross dataset recognition a survey"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "memory integrity of cnns for cross dataset facial expression recognition"):
  with members:
    - "emotion recognition using fusion of audio and video features"
    - "double transfer learning for breast cancer histopathologic image classification"
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "cross database non frontal facial expression recognition based on transductive deep transfer learning"
    - "deep facial expression recognition a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "multi target unsupervised domain adaptation without exactly shared categories"):
  with members:
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "adversarial discriminative domain adaptation"
    - "correlation alignment by riemannian metric for domain adaptation"
    - "conditional adversarial domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "transfer prototype based fuzzy clustering"
    - "deep coral correlation alignment for deep domain adaptation"
    - "unsupervised domain adaptation with residual transfer networks"

Connected through hyperedge HE-6 (induced by source paper "a comparative study on unsupervised domain adaptation approaches for coffee crop mapping"):
  with members:
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "return of frustratingly easy 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)