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: "privacy preserving neural representations of text"
  Abstract: This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user's device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.

Connected through hyperedge HE-1 (induced by source paper "predictive biases in natural language processing models a conceptual framework and overview"):
  with members:
    - "what s in a name reducing bias in bios without access to protected attributes"
    - "evaluating gender bias in machine translation"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "a framework for understanding unintended consequences of machine learning"
    - "measuring bias in contextualized word representations"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"

Connected through hyperedge HE-2 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-3 (induced by source paper "overlearning reveals sensitive attributes"):
  with members:
    - "similarity of neural network representations revisited"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "adversarial removal of demographic attributes from text data"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-4 (induced by source paper "privacy enhanced multimodal neural representations for emotion recognition"):
  with members:
    - "mirroring to build trust in digital assistants"
    - "differential privacy has disparate impact on model accuracy"
    - "racial bias in hate speech and abusive language detection datasets"
    - "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"
    - "adversarial task specific privacy preservation under attribute attack"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "a marauder s map of security and privacy in machine learning"

Connected through hyperedge HE-5 (induced by source paper "obfuscation for privacy preserving syntactic parsing"):
  with members:
    - "generalised differential privacy for text document processing"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial removal of demographic attributes from text data"
    - "towards robust and privacy preserving text representations"
    - "attention is all you need"
    - "cryptodl deep neural networks over encrypted data"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-6 (induced by source paper "generalised differential privacy for text document processing"):
  with members:
    - "the case for being average a mediocrity approach to style masking and author obfuscation"
    - "authorship verification based on compression models"
    - "learning differentially private recurrent language models"
    - "a comparison of neural models for word ordering"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "c sanitized a privacy model for document redaction and sanitization"
    - "geo indistinguishability differential privacy for location based systems"
    - "efficient estimation of word representations in vector space"

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.CL(Computation and Language)