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: "using mechanical turk to build machine translation evaluation sets"
  Abstract: Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield.

Connected through hyperedge HE-1 (induced by source paper "data cleaning for xml electronic dictionaries via statistical anomaly detection"):
  with members:
    - "translation memory retrieval methods"
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "a random forest system combination approach for error detection in digital dictionaries"
    - "detecting structural irregularity in electronic dictionaries using language modeling"
    - "correcting errors in digital lexicographic resources using a dictionary manipulation language"

Connected through hyperedge HE-2 (induced by source paper "translation memory retrieval methods"):
  with members:
    - "bucking the trend large scale cost focused active learning for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "bucking the trend large scale cost focused active learning for statistical machine translation"):
  with members:
    - "an approach to reducing annotation costs for bionlp"
    - "taking into account the differences between actively and passively acquired data the case of active learning with support vector machines for imbalanced datasets"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "rule writing or annotation cost efficient resource usage for base noun phrase chunking"

Connected through hyperedge HE-4 (induced by source paper "crowd labeling a survey"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing 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.CL(Computation and Language)