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: "bucking the trend large scale cost focused active learning for statistical machine translation"
  Abstract: We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.

Connected through hyperedge HE-1 (induced by source paper "competence based curriculum learning for neural machine translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "training tips for the transformer model"
    - "contextual parameter generation for universal neural machine translation"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "the ariel cmu systems for lorehlt18"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "domain robust feature extraction for rapid low resource asr development"
    - "sequence based multi lingual low resource speech recognition"
    - "enriching word vectors with subword information"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "active learning for interactive neural machine translation of data streams"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "online learning for effort reduction in interactive neural machine translation"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "data cleaning for xml electronic dictionaries via statistical anomaly detection"):
  with members:
    - "translation memory retrieval methods"
    - "using mechanical turk to build machine translation evaluation sets"
    - "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-5 (induced by source paper "analysis of stopping active learning based on stabilizing predictions"):
  with members:
    - "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"
    - "committee based sample selection for probabilistic classifiers"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "translation memory retrieval methods"):
  with members:
    - "using mechanical turk to build machine translation evaluation sets"

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)