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: "multi target regression via input space expansion treating targets as inputs"
  Abstract: In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common set of input variables. When the prediction targets are binary the task is called multi-label classification, while when the targets are continuous the task is called multi-target regression. In both tasks, target variables often exhibit statistical dependencies and exploiting them in order to improve predictive accuracy is a core challenge. A family of multi-label classification methods address this challenge by building a separate model for each target on an expanded input space where other targets are treated as additional input variables. Despite the success of these methods in the multi-label classification domain, their applicability and effectiveness in multi-target regression has not been studied until now. In this paper, we introduce two new methods for multi-target regression, called Stacked Single-Target and Ensemble of Regressor Chains, by adapting two popular multi-label classification methods of this family. Furthermore, we highlight an inherent problem of these methods - a discrepancy of the values of the additional input variables between training and prediction - and develop extensions that use out-of-sample estimates of the target variables during training in order to tackle this problem. The results of an extensive experimental evaluation carried out on a large and diverse collection of datasets show that, when the discrepancy is appropriately mitigated, the proposed methods attain consistent improvements over the independent regressions baseline. Moreover, two versions of Ensemble of Regression Chains perform significantly better than four state-of-the-art methods including regularization-based multi-task learning methods and a multi-objective random forest approach.

Connected through hyperedge HE-1 (induced by source paper "towards meta learning for multi target regression problems"):
  with members:
    - "a meta learning recommender system for hyperparameter tuning predicting when tuning improves svm classifiers"
    - "towards reproducible empirical research in meta learning"
    - "xgboost a scalable tree boosting system"
    - "multi target regression via random linear target combinations"

Connected through hyperedge HE-2 (induced by source paper "probabilistic regressor chains with monte carlo methods"):
  with members:
    - "scikit multiflow a multi output streaming framework"
    - "multi label methods for prediction with sequential data"
    - "deep residual learning for image recognition"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"

Connected through hyperedge HE-3 (induced by source paper "learning generalisable coupling terms for obstacle avoidance via low dimensional geometric descriptors"):
  with members:
    - "learning and generalisation of primitives skills towards robust dual arm manipulation"
    - "learning sensor feedback models from demonstrations via phase modulated neural networks"
    - "learning feedback terms for reactive planning and control"

Connected through hyperedge HE-4 (induced by source paper "online multi target regression trees with stacked leaf models"):
  with members:
    - "scikit multiflow a multi output streaming framework"
    - "openml networked science in machine learning"

Connected through hyperedge HE-5 (induced by source paper "machine learning for predicting thermal power consumption of the mars express spacecraft"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "top down induction of clustering trees"

Connected through hyperedge HE-6 (induced by source paper "outlier robust extreme learning machine for multi target regression"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"

Connected through hyperedge HE-7 (induced by source paper "diversity and degrees of freedom in regression ensembles"):
  with members:
    - "degrees of freedom in deep neural networks"
    - "all you need is a good init"
    - "deep learning in neural networks"
    - "modular autoencoders for ensemble feature extraction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "dynamic classifier chains for multi label learning"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "multi instance multi label learning"

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)