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: "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"
  Abstract: Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency. However, the learned hash codes are embedded with limited discriminative semantics due to the intrinsic limitation of image representation. To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH). The key idea is to \emph{directly} augment the semantics of discrete image hash codes by exploring auxiliary contextual modalities. To this end, a unified hashing framework is formulated to simultaneously preserve visual similarities of images and perform semantic transfer from contextual modalities. Further, to guarantee direct semantic transfer and avoid information loss, we explicitly impose the discrete constraint, bit--uncorrelation constraint and bit-balance constraint on hash codes. A novel and effective discrete optimization method based on augmented Lagrangian multiplier is developed to iteratively solve the optimization problem. The whole learning process has linear computation complexity and desirable scalability. Experiments on three benchmark datasets demonstrate the superiority of DSTH compared with several state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "locality preserving joint transfer for domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot hashing for multi label image retrieval"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "hashing in the zero shot framework with domain adaptation"
    - "hashnet deep learning to hash by continuation"
    - "scalable image retrieval by sparse product quantization"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "deep semantic ranking based hashing for multi label image retrieval"

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.IR(Information Retrieval)