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: "distributional semantics beyond words supervised learning of analogy and paraphrase"
  Abstract: There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval~2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "an analysis on the learning rules of the skip gram model"):
  with members:
    - "competitive learning enriches learning representation and accelerates the fine tuning of cnns"
    - "an exploration of word embedding initialization in deep learning tasks"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-3 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space"):
  with members:
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "analogical inference for multi relational embeddings"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "complex embeddings for simple link prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "holographic embeddings of knowledge graphs"
    - "embedding semantic relations into word representations"

Connected through hyperedge HE-4 (induced by source paper "textual analogy parsing what s shared and what s compared among analogous facts"):
  with members:
    - "end to end neural coreference resolution"
    - "how much is 131 million dollars putting numbers in perspective with compositional descriptions"
    - "neural semantic role labeling with dependency path embeddings"
    - "neural architectures for named entity recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "parallelizing word2vec in shared and distributed memory"):
  with members:
    - "adabatch adaptive batch sizes for training deep neural networks"
    - "parallelizing word2vec in multi core and many core architectures"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "effective approaches to attention based neural machine translation"
    - "splash user friendly programming interface for parallelizing stochastic algorithms"
    - "ask me anything dynamic memory networks for natural language processing"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "feature extraction using latent dirichlet allocation and neural networks a case study on movie synopses"):
  with members:
    - "learning multilingual word representations using a bag of words autoencoder"
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "expectation propogation for the generative aspect model"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-7 (induced by source paper "conceptnet 5 5 an open multilingual graph of general knowledge"):
  with members:
    - "an ensemble method to produce high quality word embeddings"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "holographic embeddings of knowledge graphs"
    - "efficient estimation of word representations in vector space"
    - "bagpack a general framework to represent semantic relations"
    - "similarity of semantic relations"

Connected through hyperedge HE-8 (induced by source paper "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a latent variable model approach to pmi based word embeddings"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "incorporating both distributional and relational semantics in word representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)