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: "auto weka combined selection and hyperparameter optimization of classification algorithms"
  Abstract: Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We consider the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous work that addresses these issues in isolation. We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA, spanning 2 ensemble methods, 10 meta-methods, 27 base classifiers, and hyperparameter settings for each classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup 09, variants of the MNIST dataset and CIFAR-10, we show classification performance often much better than using standard selection/hyperparameter optimization methods. We hope that our approach will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.

Connected through hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"

Connected through hyperedge HE-2 (induced by source paper "extreme algorithm selection with dyadic feature representation"):
  with members:
    - "automated algorithm selection survey and perspectives"
    - "aslib a benchmark library for algorithm selection"
    - "openml networked science in machine learning"
    - "a theoretical analysis of ndcg type ranking measures"
    - "restart strategy selection using machine learning techniques"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-3 (induced by source paper "automatic machine learning derived from scholarly big data"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "wikiometrics a wikipedia based ranking system"
    - "neural architecture search with reinforcement learning"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-4 (induced by source paper "an adaptive and near parameter free evolutionary computation approach towards true automation in automl"):
  with members:
    - "an open source automl benchmark"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "a large population size can be unhelpful in evolutionary algorithms"

Connected through hyperedge HE-5 (induced by source paper "improving generalisation of automl systems with dynamic fitness evaluations"):
  with members:
    - "an open source automl benchmark"
    - "towards automated machine learning evaluation and comparison of automl approaches and tools"
    - "regularized evolution for image classifier architecture search"
    - "evaluation of a tree based pipeline optimization tool for automating data science"

Connected through hyperedge HE-6 (induced by source paper "learnergy energy based machine learners"):
  with members:
    - "adversarially robust generalization requires more data"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "avatar machine learning pipeline evaluation using surrogate model"):
  with members:
    - "survey on automated machine learning"
    - "benchmarking automatic machine learning frameworks"

Connected through hyperedge HE-8 (induced by source paper "caai a cognitive architecture to introduce artificial intelligence in cyber physical production systems"):
  with members:
    - "evaluation of cognitive architectures for cyber physical production systems"

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)