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: "efficient non greedy optimization of decision trees"
  Abstract: Decision trees and randomized forests are widely used in computer vision and machine learning. Standard algorithms for decision tree induction optimize the split functions one node at a time according to some splitting criteria. This greedy procedure often leads to suboptimal trees. In this paper, we present an algorithm for optimizing the split functions at all levels of the tree jointly with the leaf parameters, based on a global objective. We show that the problem of finding optimal linear-combination (oblique) splits for decision trees is related to structured prediction with latent variables, and we formulate a convex-concave upper bound on the tree's empirical loss. The run-time of computing the gradient of the proposed surrogate objective with respect to each training exemplar is quadratic in the the tree depth, and thus training deep trees is feasible. The use of stochastic gradient descent for optimization enables effective training with large datasets. Experiments on several classification benchmarks demonstrate that the resulting non-greedy decision trees outperform greedy decision tree baselines.

Connected through hyperedge HE-1 (induced by source paper "neural packet classification"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning to search with mctsnets"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "internet congestion control via deep reinforcement learning"
    - "autoaugment learning augmentation policies from data"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-2 (induced by source paper "training robust tree ensembles for security"):
  with members:
    - "detecting and characterizing lateral phishing at scale"
    - "gradient regularized budgeted boosting"
    - "robust decision trees against adversarial examples"
    - "robustness verification of tree based models"
    - "multi layered gradient boosting decision trees"
    - "adgraph a graph based approach to ad and tracker blocking"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "locally constant networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "on the convergence of adam and beyond"
    - "moleculenet a benchmark for molecular machine learning"
    - "densely connected convolutional networks"
    - "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
    - "deep residual learning for image recognition"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-4 (induced by source paper "gradient boosted decision tree neural network"):
  with members:
    - "deep neural decision trees"
    - "xgboost a scalable tree boosting system"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "efficient regularized piecewise linear regression trees"):
  with members:
    - "an inequality with applications to structured sparsity and multitask dictionary learning"
    - "structured sparsity and generalization"

Connected through hyperedge HE-6 (induced by source paper "deep neural decision trees"):
  with members:
    - "challenges for transparency"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "hierarchical multiscale recurrent neural networks"
    - "deep learning in neural networks"
    - "estimating or propagating gradients through stochastic neurons"

Connected through hyperedge HE-7 (induced by source paper "column generation based math heuristic for classification trees"):
  with members:
    - "boolean decision rules via column generation"
    - "auction optimization using regression trees and linear models as integer programs"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-8 (induced by source paper "random forests versus neural networks what s best for camera localization"):
  with members:
    - "dsac differentiable ransac for camera localization"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)