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: "unified adversarial invariance"
  Abstract: We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions. Invariance to nuisance is achieved by learning a split representation of data through competitive training between the prediction task and a reconstruction task coupled with disentanglement, whereas that to biasing factors is brought about by penalizing the network if the latent embedding contains any information about them. We describe an adversarial instantiation of this framework and provide analysis of its working. Our model outperforms previous works at inducing invariance to nuisance factors without using any labeled information about such variables, and achieves state-of-the-art performance at learning independence to biasing factors in fairness settings.

Connected through hyperedge HE-1 (induced by source paper "discovery and separation of features for invariant representation learning"):
  with members:
    - "invariant representations through adversarial forgetting"
    - "exact rate distortion in autoencoders via echo noise"
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-2 (induced by source paper "invariant representations through adversarial forgetting"):
  with members:
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"
    - "dynamic routing between capsules"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-3 (induced by source paper "learning fair and interpretable representations via linear orthogonalization"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "on the fairness of disentangled representations"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "the frontiers of fairness in machine learning"
    - "a convex framework for fair regression"
    - "controllable invariance through adversarial feature learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-4 (induced by source paper "niesr nuisance invariant end to end speech recognition"):
  with members:
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "very deep convolutional networks for end to end speech recognition"
    - "invariant representations for noisy speech recognition"
    - "listen attend and spell"
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"

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)