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: "invariant representations without adversarial training"
  Abstract: Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance to arbitrary nuisance factors yet remain useful for other tasks is challenging. Existing approaches cast the trade-off between task performance and invariance in an adversarial way, using an iterative minimax optimization. We show that adversarial training is unnecessary and sometimes counter-productive; we instead cast invariant representation learning as a single information-theoretic objective that can be directly optimized. We demonstrate that this approach matches or exceeds performance of state-of-the-art adversarial approaches for learning fair representations and for generative modeling with controllable transformations.

Connected through hyperedge HE-1 (induced by source paper "adversarial disentanglement with grouped observations"):
  with members:
    - "disentangling content and style via unsupervised geometry distillation"
    - "disentangling style and content in anime illustrations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "unsupervised adversarial invariance"

Connected through hyperedge HE-2 (induced by source paper "learning unbiased representations via mutual information backpropagation"):
  with members:
    - "on variational bounds of mutual information"
    - "flexibly fair representation learning by disentanglement"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "beyond local nash equilibria for adversarial networks"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"
    - "generalizing across domains via cross gradient training"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "invariant representations through adversarial forgetting"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant neural networks and equivarification"
    - "neural ordinary differential equations"
    - "learning steerable filters for rotation equivariant cnns"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "inverse learning of symmetry transformations"):
  with members:
    - "learning extremal representations with deep archetypal analysis"
    - "deep archetypal analysis"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "invariant representations through adversarial forgetting"
    - "adversarial information factorization"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning latent subspaces in variational autoencoders"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

Connected through hyperedge HE-6 (induced by source paper "on variational bounds of mutual information"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "formal limitations on the measurement of mutual information"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "representation learning with contrastive predictive coding"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "man is to person as woman is to location measuring gender bias in named entity recognition"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "a survey on bias and fairness in machine learning"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "gender bias in contextualized word embeddings"
    - "identifying and reducing gender bias in word level language models"

Connected through hyperedge HE-8 (induced by source paper "discovery and separation of features for invariant representation learning"):
  with members:
    - "unified adversarial invariance"
    - "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"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"

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)