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: "deep neural decision trees"
  Abstract: Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. We evaluate DNDT on several tabular datasets, verify its efficacy, and investigate similarities and differences between DNDT and vanilla decision trees. Interestingly, DNDT self-prunes at both split and feature-level.

Connected through hyperedge HE-1 (induced by source paper "deep differentiable forest with sparse attention for the tabular data"):
  with members:
    - "litemort a memory efficient gradient boosting tree system on adaptive compact distributions"
    - "neural oblivious decision ensembles for deep learning on tabular data"
    - "sparse sequence to sequence models"
    - "optimization methods for interpretable differentiable decision trees in reinforcement learning"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "adaptive neural trees"
    - "catboost gradient boosting with categorical features support"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "a framework for democratizing ai"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "a generic framework for privacy preserving deep learning"
    - "auto keras an efficient neural architecture search system"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-4 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"
    - "adaptive neural trees"

Connected through hyperedge HE-5 (induced by source paper "tabnet attentive interpretable tabular learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "consistent individualized feature attribution for tree ensembles"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive neural trees"
    - "xgboost scalable gpu accelerated learning"
    - "learning to explain an information theoretic perspective on model interpretation"

Connected through hyperedge HE-6 (induced by source paper "actionable interpretability through optimizable counterfactual explanations for tree ensembles"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "efficient search for diverse coherent explanations"
    - "model agnostic contrastive explanations for structured data"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "generating counterfactual and contrastive explanations using shap"
    - "interpretable credit application predictions with counterfactual explanations"
    - "local rule based explanations of black box decision systems"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-7 (induced by source paper "neural oblivious decision ensembles for deep learning on tabular data"):
  with members:
    - "sparse sequence to sequence models"
    - "multi layered gradient boosting decision trees"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "sparsemax and relaxed wasserstein for topic sparsity"
    - "averaging weights leads to wider optima and better generalization"
    - "catboost unbiased boosting with categorical features"
    - "deep forest towards an alternative to deep neural networks"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-8 (induced by source paper "making classical machine learning pipelines differentiable a neural translation approach"):
  with members:
    - "machine learning at microsoft with ml net"
    - "achieving human parity on automatic chinese to english news translation"
    - "differentiable scheduled sampling for credit assignment"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)