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: "variational deep semantic hashing for text documents"
  Abstract: As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. #R##N#In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.

Connected through hyperedge HE-1 (induced by source paper "searching a database of source codes using contextualized code search"):
  with members:
    - "multi modal attention network learning for semantic source code retrieval"
    - "on learning meaningful code changes via neural machine translation"
    - "when deep learning met code search"
    - "mapping language to code in programmatic context"
    - "tree2tree neural translation model for learning source code changes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-2 (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"
    - "a survey on learning to hash"
    - "tensorflow a system for large scale machine learning"
    - "character level convolutional networks for text classification"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-3 (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"
    - "a survey on learning to hash"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "variational lossy autoencoder"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "exs explainable search using local model agnostic interpretability"):
  with members:
    - "posthoc interpretability of learning to rank models using secondary training data"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "neural models for information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "a dual embedding space model for document ranking"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "wikipedia text reuse within and without"):
  with members:
    - "patterns of text reuse in a scientific corpus"
    - "global disease monitoring and forecasting with wikipedia"
    - "identifying duplicate and contradictory information in wikipedia"

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)