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: "semi supervised learning via compact latent space clustering"
  Abstract: We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.

Connected through hyperedge HE-1 (induced by source paper "end to end graph based deep semi supervised learning"):
  with members:
    - "randaugment practical automated data augmentation with a reduced search space"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-2 (induced by source paper "infinitely wide graph convolutional networks semi supervised learning via gaussian processes"):
  with members:
    - "graph convolutional gaussian processes"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "improving landmark localization with semi supervised learning"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-3 (induced by source paper "semi supervised few shot learning with prototypical random walks"):
  with members:
    - "edge labeling graph neural network for few shot learning"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "low shot learning with large scale diffusion"
    - "good semi supervised learning that requires a bad gan"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-4 (induced by source paper "domain generalization via model agnostic learning of semantic features"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "on first order meta learning algorithms"
    - "learning to adapt structured output space for semantic segmentation"
    - "generalizing across domains via cross gradient training"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "generalizing to unseen domains via adversarial data augmentation"

Connected through hyperedge HE-5 (induced by source paper "manifold graph with learned prototypes for semi supervised image classification"):
  with members:
    - "multi class classification without multi class labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "relational inductive biases deep learning and graph networks"
    - "deep co training for semi supervised image recognition"
    - "data efficient graph embedding learning for pcb component detection"
    - "dynamic graph cnn for learning on point clouds"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "patch level neighborhood interpolation a general and effective graph based regularization strategy"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "mixmatch a holistic approach to semi supervised learning"
    - "tangent normal adversarial regularization for semi supervised learning"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning with scarce annotations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "semi supervised learning by label gradient alignment"
    - "revisiting self supervised visual representation learning"
    - "unsupervised representation learning by predicting image rotations"
    - "when semi supervised learning meets transfer learning training strategies models and datasets"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "the reversible residual network backpropagation without storing activations"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-8 (induced by source paper "improving distantly supervised entity typing with compact latent space clustering"):
  with members:
    - "a unified labeling approach by pooling diverse datasets for entity typing"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "attention is all you need"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "corpus level fine grained entity typing using contextual information"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"

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)