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: "optimal sparse decision trees"
  Abstract: Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack of guarantees of closeness to optimality: decision tree algorithms are often greedy or myopic, and sometimes produce unquestionably suboptimal models. Hardness of decision tree optimization is both a theoretical and practical obstacle, and even careful mathematical programming approaches have not been able to solve these problems efficiently. This work introduces the first practical algorithm for optimal decision trees for binary variables. The algorithm is a co-design of analytical bounds that reduce the search space and modern systems techniques, including data structures and a custom bit-vector library. We highlight possible steps to improving the scalability and speed of future generations of this algorithm based on insights from our theory and experiments.

Connected through hyperedge HE-1 (induced by source paper "decision trees for decision making under the predict then optimize framework"):
  with members:
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "generalization bounds in the predict then optimize framework"
    - "end to end learning and optimization on graphs"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "task based end to end model learning in stochastic optimization"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-2 (induced by source paper "suod a scalable unsupervised outlier detection framework"):
  with members:
    - "suod toward scalable unsupervised outlier detection"
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "pyod a python toolbox for scalable outlier detection"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "generative adversarial active learning for unsupervised outlier detection"
    - "semi supervised rare disease detection using generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "the secrets of machine learning ten things you wish you had known earlier to be more effective at data analysis"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "this looks like that deep learning for interpretable image recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "sanity checks for saliency maps"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "progressive growing of gans for improved quality stability and variation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)