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: "right for the right reason training agnostic networks"
  Abstract: We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a “protected concept”, that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of “being right for the right reason”, where we request a classifier to make a decision in a way that is entirely ‘agnostic’ to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an ‘agnostic model’, we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.

Connected through hyperedge HE-1 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "rank consistent ordinal regression for neural networks"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-2 (induced by source paper "rubi reducing unimodal biases in visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "explicit bias discovery in visual question answering models"
    - "bilinear attention networks"
    - "from recognition to cognition visual commonsense reasoning"
    - "women also snowboard overcoming bias in captioning models"
    - "vizwiz grand challenge answering visual questions from blind people"

Connected through hyperedge HE-3 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models"):
  with members:
    - "explicit bias discovery in visual question answering models"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "learning gender neutral word embeddings"
    - "gender bias in coreference resolution evaluation and debiasing methods"

Connected through hyperedge HE-4 (induced by source paper "sensitivenets learning agnostic representations with application to face recognition"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "yfcc100m the new data in multimedia research"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "flowsan privacy enhancing semi adversarial networks to confound arbitrary face based gender classifiers"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a survey to deep facial attribute analysis"
    - "using lip to gloss over faces in single stage face detection networks"

Connected through hyperedge HE-6 (induced by source paper "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"):
  with members:
    - "explanation methods in deep learning users values concerns and challenges"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "adversarial discriminative domain adaptation"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "the megaface benchmark 1 million faces for recognition at scale"

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.LG(Machine Learning)