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: "self taught hashing for fast similarity search"
  Abstract: The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal $l$-bit binary codes for all documents in the given corpus via unsupervised learning, and then train $l$ classifiers via supervised learning to predict the $l$-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.

Connected through hyperedge HE-1 (induced by source paper "learning compressed sentence representations for on device text processing"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "improving sentence representations with multi view frameworks"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-2 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "deep region hashing for efficient large scale instance search from images"
    - "supervising neural attention models for video captioning by human gaze data"
    - "fooling vision and language models despite localization and attention mechanism"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "unsupervised neural generative semantic hashing"):
  with members:
    - "neural check worthiness ranking with weak supervision finding sentences for fact checking"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "large scale gan training for high fidelity natural image synthesis"
    - "neural ranking models with weak supervision"
    - "variational deep semantic hashing for text documents"
    - "a survey on learning to hash"
    - "tensorflow a system for large scale machine learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "document hashing with mixture prior generative models"):
  with members:
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "variational deep semantic hashing for text documents"
    - "a survey on learning to hash"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "variational lossy autoencoder"

Connected through hyperedge HE-5 (induced by source paper "on the evaluation metric for hashing"):
  with members:
    - "deep supervised discrete hashing"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "how should we evaluate supervised hashing"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "learning hash functions using column generation"

Connected through hyperedge HE-6 (induced by source paper "learning hash function through codewords"):
  with members:
    - "hash function learning via codewords"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning hash functions using column generation"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-7 (induced by source paper "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning to hash for indexing big data a survey"
    - "supervised hashing using graph cuts and boosted decision trees"

Connected through hyperedge HE-8 (induced by source paper "nash toward end to end neural architecture for generative semantic hashing"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "deep supervised discrete hashing"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "adaptive convolutional filter generation for natural language understanding"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "stochastic generative hashing"
    - "learning k way d dimensional discrete code for compact embedding representations"

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)