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 conditional random field for discriminatively trained finite state string edit distance"
  Abstract: The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.

Connected through hyperedge HE-1 (induced by source paper "metric learning for ordered labeled trees with pq grams"):
  with members:
    - "tree edit distance learning via adaptive symbol embeddings"
    - "revisiting the tree edit distance and its backtracing a tutorial"
    - "adam a method for stochastic optimization"
    - "a survey on metric learning for feature vectors and structured data"
    - "an optimal decomposition algorithm for tree edit distance"

Connected through hyperedge HE-2 (induced by source paper "an effective approach to unsupervised machine translation"):
  with members:
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised statistical machine translation"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-3 (induced by source paper "optimal transport based alignment of learned character representations for string similarity"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "attention is all you need"
    - "character level deep conflation for business data analytics"
    - "a decomposable attention model for natural language inference"
    - "random forest dbscan for uspto inventor name disambiguation"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "robust group linkage"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-4 (induced by source paper "attention based encoder decoder networks for spelling and grammatical error correction"):
  with members:
    - "grammatical error correction with neural reinforcement learning"
    - "gleu without tuning"
    - "effective approaches to attention based neural machine translation"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a survey on metric learning for feature vectors and structured data"):
  with members:
    - "robustness and generalization for metric learning"
    - "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd"
    - "large margin metric learning for partitioning problems"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "parametric local metric learning for nearest neighbor classification"
    - "supervised metric learning with generalization guarantees"

Connected through hyperedge HE-6 (induced by source paper "end to end text recognition with hybrid hmm maxout models"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "supervised metric learning with generalization guarantees"):
  with members:
    - "robustness and generalization for metric learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "information theoretic semi supervised metric learning via entropy regularization"
    - "supervised learning with similarity functions"
    - "non sparse linear representations for visual tracking with online reservoir metric learning"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "adaptive regularization for weight matrices"
    - "learning neighborhoods for metric learning"

Connected through hyperedge HE-8 (induced by source paper "learning document level semantic properties from free text annotations"):
  with members:
    - "modeling online reviews with multi grain topic models"
    - "more accurate tests for the statistical significance of result differences"
    - "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies"
    - "objective criteria for the evaluation of clustering methods"

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)