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: "temporal cross media retrieval with soft smoothing"
  Abstract: Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.

Connected through hyperedge HE-1 (induced by source paper "diachronic cross modal embeddings"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "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-2 (induced by source paper "cross modal subspace learning with scheduled adaptive margin constraints"):
  with members:
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "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"

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)