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: "pmlb a large benchmark suite for machine learning evaluation and comparison"
  Abstract: The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Connected through hyperedge HE-1 (induced by source paper "the tree ensemble layer differentiability meets conditional computation"):
  with members:
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning deep nearest neighbor representations using differentiable boundary trees"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "xgboost a scalable tree boosting system"
    - "decision forests convolutional networks and the models in between"
    - "conditional computation in neural networks for faster models"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "analytic continued fractions for regression a memetic algorithm approach"):
  with members:
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "block building programming for symbolic regression"
    - "xgboost a scalable tree boosting system"
    - "epsilon lexicase selection for regression"
    - "adam a method for stochastic optimization"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-3 (induced by source paper "neural rule ensembles encoding sparse feature interactions into neural networks"):
  with members:
    - "decision forests convolutional networks and the models in between"
    - "adam a method for stochastic optimization"
    - "interpreting tree ensembles with intrees"

Connected through hyperedge HE-4 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint"):
  with members:
    - "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"
    - "a memoization framework for scaling submodular optimization to large scale problems"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "stochastic greedy closing the optimality gap in exact weak submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "greed is good near optimal submodular maximization via greedy optimization"

Connected through hyperedge HE-5 (induced by source paper "neural network architecture search with differentiable cartesian genetic programming for regression"):
  with members:
    - "eat nas elastic architecture transfer for accelerating large scale neural architecture search"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "the quest for the golden activation function"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"
    - "searching for activation functions"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-6 (induced by source paper "flo fast and lightweight hyperparameter optimization for automl"):
  with members:
    - "an open source automl benchmark"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "automating biomedical data science through tree based pipeline optimization"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"

Connected through hyperedge HE-7 (induced by source paper "darwinml a graph based evolutionary algorithm for automated machine learning"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "progressive neural architecture search"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "large scale evolution of image classifiers"
    - "limits of end to end learning"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "automating biomedical data science through tree based pipeline optimization"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "natively interpretable machine learning and artificial intelligence preliminary results and future directions"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "why should i trust you explaining the predictions of any classifier"
    - "maximizing diversity in biology and beyond"
    - "deep learning and the information bottleneck principle"
    - "falling rule lists"

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)