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 "nash toward end to end neural architecture for generative semantic hashing"):
  Abstract: Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables. A neural variational inference framework is proposed for training, where gradients are directly back-propagated through the discrete latent variable to optimize the hash function. We also draw connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of the proposed framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both unsupervised and supervised scenarios.

Includes members:
  Member "baseline needs more love on simple word embedding based models and associated pooling mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta embeddings based on self attention")
  Member "deep supervised discrete hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")
  Member "topic compositional neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformer xl attentive language models beyond a fixed length context")
  Member "deconvolutional latent variable model for text sequence matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "topic guided variational autoencoders for text generation")
  Member "adaptive convolutional filter generation for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semantic sentence matching with densely connected recurrent and co attentive information")
  Member "improved variational autoencoders for text modeling using dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "stochastic generative hashing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "learning k way d dimensional discrete code for compact embedding representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning multi granular quantized embeddings for large vocab categorical features in recommender systems")

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.CL(Computation and Language)