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: "composite correlation quantization for efficient multimodal retrieval"
  Abstract: Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in achieving this goal, current attempts generally fail to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities in a continuous isomorphic space and separately threshold embeddings into binary codes, which incurs substantial loss of retrieval accuracy. In this paper, we approach seamless multimodal hashing by proposing a novel Composite Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into isomorphic latent space, and learns composite quantizers that convert the isomorphic latent features into compact binary codes. An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors, which can be trained from both paired and partially paired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval.

Connected through hyperedge HE-1 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "collaborative quantization for cross modal similarity search"
    - "deep binary reconstruction for cross modal hashing"
    - "deep cross modal hashing"
    - "stacked quantizers for compositional vector compression"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-2 (induced by source paper "quantifying and alleviating the language prior problem in visual question answering"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "learning to count objects in natural images for visual question answering"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "shared predictive cross modal deep quantization"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "fast supervised discrete hashing"
    - "supervised discrete hashing with relaxation"
    - "collaborative quantization for cross modal similarity search"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "deep cross modal hashing"
    - "domain separation networks"

Connected through hyperedge HE-4 (induced by source paper "deep triplet quantization"):
  with members:
    - "subic a supervised structured binary code for image search"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "optimized cartesian k means"

Connected through hyperedge HE-5 (induced by source paper "harmonized multimodal learning with gaussian process latent variable models"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "deep multimodal representation learning from temporal data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep cross modal hashing"
    - "training and evaluating multimodal word embeddings with large scale web annotated images"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "variational auto encoded deep gaussian processes"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-6 (induced by source paper "effective and efficient indexing in cross modal hashing based datasets"):
  with members:
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "discrete latent factor model for cross modal hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "deep cross modal hashing"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-7 (induced by source paper "sch gan semi supervised cross modal hashing by generative adversarial network"):
  with members:
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "perceptual generative adversarial networks for small object detection"
    - "unsupervised learning for physical interaction through video prediction"
    - "generative adversarial text to image synthesis"
    - "deep cross modal hashing"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"):
  with members:
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "fast training of triplet based deep binary embedding networks"
    - "very deep convolutional networks for natural language processing"
    - "very deep convolutional networks for text classification"
    - "grounding of textual phrases in images by reconstruction"
    - "deep cross modal hashing"
    - "learning to hash with binary deep neural network"
    - "multimodal convolutional neural networks for matching image and sentence"

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)