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: "decomposition based transfer distance metric learning for image classification"
  Abstract: Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.

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"
    - "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"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "cost sensitive feature selection by optimizing f measures"):
  with members:
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-3 (induced by source paper "calibrated steganalysis of mp3stego in multi encoder scenario"):
  with members:
    - "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario"
    - "comprehensive review of audio steganalysis methods"
    - "universal audio steganalysis based on calibration and reversed frequency resolution of human auditory system"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "unsupervised feature selection with adaptive structure learning"
    - "generalized fisher score for feature selection"

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)