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: "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
  Abstract: Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data. However, existing methods based on DNN face the challenge of insufficient cross-modal training data, which limits the training effectiveness and easily leads to overfitting. Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain. Such large-scale single-modal datasets also contain rich modal-independent semantic knowledge that can be shared across different modalities. Besides, large-scale cross-modal datasets are very labor-consuming to collect and label, so it is significant to fully exploit the knowledge in single-modal datasets for boosting cross-modal retrieval. This paper proposes modal-adversarial hybrid transfer network (MHTN), which to the best of our knowledge is the first work to realize knowledge transfer from single-modal source domain to cross-modal target domain, and learn cross-modal common representation. It is an end-to-end architecture with two subnetworks: (1) Modal-sharing knowledge transfer subnetwork is proposed to jointly transfer knowledge from a large-scale single-modal dataset in source domain to all modalities in target domain with a star network structure, which distills modal-independent supplementary knowledge for promoting cross-modal common representation learning. (2) Modal-adversarial semantic learning subnetwork is proposed to construct an adversarial training mechanism between common representation generator and modality discriminator, making the common representation discriminative for semantics but indiscriminative for modalities to enhance cross-modal semantic consistency during transfer process. Comprehensive experiments on 4 widely-used datasets show its effectiveness and generality.

Connected through hyperedge HE-1 (induced by source paper "a new benchmark and approach for fine grained cross media retrieval"):
  with members:
    - "fine grained video categorization with redundancy reduction attention"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "fine grained image classification via combining vision and language"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "diachronic cross modal embeddings"):
  with members:
    - "temporal cross media retrieval with soft smoothing"
    - "dynamic word embeddings for evolving semantic discovery"
    - "a comprehensive survey on cross modal retrieval"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "learning deep structure preserving image text embeddings"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-3 (induced by source paper "cross modal subspace learning with scheduled adaptive margin constraints"):
  with members:
    - "temporal cross media retrieval with soft smoothing"
    - "a comprehensive survey on cross modal retrieval"
    - "learning deep structure preserving image text embeddings"
    - "facenet a unified embedding for face recognition and clustering"
    - "maximum margin structured learning with deep networks for 3d human pose estimation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "a multi view embedding space for modeling internet images tags and their semantics"

Connected through hyperedge HE-4 (induced by source paper "adversarial cross modal retrieval via learning and transferring single modal similarities"):
  with members:
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "a multi view embedding space for modeling internet images tags and their semantics"

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.MM(Multimedia)