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: "information theoretical label embeddings for large scale image classification"
  Abstract: We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed vectors, and treating the classification problem as a cosine proximity regression problem on this sphere. We test our method on a dataset of 300 million high-resolution images with 17,000 labels, where it yields considerably faster convergence, as well as a 7% higher mean average precision compared to logistic regression.

Connected through hyperedge HE-1 (induced by source paper "neural word search in historical manuscript collections"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "identity mappings in deep residual networks"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-2 (induced by source paper "large scale visual relationship understanding"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "an interpretable model for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised object detection with pointwise mutual information"):
  with members:
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "weakly supervised deep detection networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "getting deep recommenders fit bloom embeddings for sparse binary input output networks"):
  with members:
    - "wide deep learning for recommender systems"
    - "efficient softmax approximation for gpus"
    - "label embedding for image classification"
    - "hybrid recommender system based on autoencoders"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "inferring networks of substitutable and complementary products"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "attribute cnns for word spotting in handwritten documents"):
  with members:
    - "learning deep representations for word spotting under weak supervision"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "separating self expression and visual content in hashtag supervision"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the devil is in the tails fine grained classification in the wild"
    - "understanding infographics through textual and visual tag prediction"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning visual n grams from web data"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"

Connected through hyperedge HE-7 (induced by source paper "a deep multimodal approach for cold start music recommendation"):
  with members:
    - "neural based noise filtering from word embeddings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "knowledge matters importance of prior information for optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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.CV(Computer Vision and Pattern Recognition)