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: "a fast and robust tsvm for pattern classification"
  Abstract: Twin support vector machine~(TSVM) is a powerful learning algorithm by solving a pair of smaller SVM-type problems. However, there are still some specific issues waiting to be solved when it faces with some real applications, \emph{e.g}, low efficiency and noise data. In this paper, we propose a Fast and Robust TSVM~(FR-TSVM) to deal with these issues above. In FR-TSVM, we propose an effective fuzzy membership function to ease the effects of noisy inputs. We apply the fuzzy membership to each input instance and reformulate the TSVMs such that different input instances can make different contributions to the learning of the separating hyperplanes. To further speed up the training procedure, we develop an efficient coordinate descent algorithm with shirking to solve the involved a pair of quadratic programming problems (QPPs) of FR-TSVM. Moreover, theoretical foundations of the proposed model are analyzed in details. The experimental results on several artificial and benchmark datasets indicate that the FR-TSVM not only obtains the fast learning speed but also shows the robust classification performance.

Connected through hyperedge HE-1 (induced by source paper "credit scoring for micro loans"):
  with members:
    - "towards global explanations for credit risk scoring"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "fairness through awareness"
    - "learning from labeled and unlabeled data an empirical study across techniques and domains"

Connected through hyperedge HE-2 (induced by source paper "incremental and decremental fuzzy bounded twin support vector machine"):
  with members:
    - "fastfood approximate kernel expansions in loglinear time"

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)