human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"):
  Abstract: Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamming space for matching. Most of the traditional textual-visual binary encoding methods only consider holistic image representations and fail to model descriptive sentences. This renders existing methods inappropriate to handle the rich semantics of informative cross-modal data for quality textual-visual search tasks. To address the problem of hashing cross-modal data with semantic-rich cues, in this paper, a novel integrated deep architecture is developed to effectively encode the detailed semantics of informative images and long descriptive sentences, named as Textual-Visual Deep Binaries (TVDB). In particular, region-based convolutional networks with long short-term memory units are introduced to fully explore image regional details while semantic cues of sentences are modeled by a text convolutional network. Additionally, we propose a stochastic batch-wise training routine, where high-quality binary codes and deep encoding functions are efficiently optimized in an alternating manner. Experiments are conducted on three multimedia datasets, i.e. Microsoft COCO, IAPR TC-12, and INRIA Web Queries, where the proposed TVDB model significantly outperforms state-of-the-art binary coding methods in the task of cross-modal retrieval.

Includes members:
  Member "deep sketch hashing fast free hand sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a network pruning network approach to deep model compression")
  Member "fast training of triplet based deep binary embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "very deep convolutional networks for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "description based text classification with reinforcement learning")
  Member "very deep convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "composite correlation quantization for efficient multimodal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval")
  Member "grounding of textual phrases in images by reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "deep cross modal hashing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "learning to hash with binary deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep hashing methods")

Question: Please tell me which class the source paper 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)