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 "privacy protection for natural language records neural generative models for releasing synthetic twitter data"):
  Abstract: In this paper we consider methods for sharing free text Twitter data, with the goal of protecting the privacy of individuals in the data while still releasing data that carries research value, i.e. minimizes risk and maximizes utility. We propose three protection methods: simple redaction of hashtags and twitter handles, an epsilon-differentially private Multinomial-Dirichlet synthesizer, and novel synthesis models based on a neural generative model. We evaluate these three methods using empirical measures of risk and utility. We define risk based on possible identification of users in the Twitter data, and we define utility based on two general language measures and two model-based tasks. We find that redaction maintains high utility for simple tasks but at the cost of high risk, while some neural synthesis models are able to produce higher levels of utility, even for more complicated tasks, while maintaining lower levels of risk. In practice, utility and risk present a trade-off, with some methods offering lower risk or higher utility. This work presents possible methods to approach the problem of privacy for free text and which methods could be used to meet different utility and risk thresholds.

Includes members:
  Member "a persona based neural conversation model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "a simple way to initialize recurrent networks of rectified linear units":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "a neural network approach to context sensitive generation of conversational responses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non autoregressive neural dialogue generation")
  Member "gated feedback recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spiking neural predictive coding for continual learning from data streams")
  Member "a neural conversational model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention over parameters for dialogue systems")
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "generating sequences with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)