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: "the latent relation mapping engine algorithm and experiments"
  Abstract: Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.

Connected through hyperedge HE-1 (induced by source paper "a survey of word embeddings evaluation methods"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "an exploration of word embedding initialization in deep learning tasks"
    - "hypothesis testing based intrinsic evaluation of word embeddings"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "automated detection of non relevant posts on the russian imageboard 2ch importance of the choice of word representations"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"

Connected through hyperedge HE-4 (induced by source paper "analogical inference for multi relational embeddings"):
  with members:
    - "cross graph learning of multi relational associations"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "traversing knowledge graphs in vector space"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "distributional semantics beyond words supervised learning of analogy and paraphrase"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "domain and function a dual space model of semantic relations and compositions"
    - "a context theoretic framework for compositionality in distributional semantics"
    - "from frequency to meaning vector space models of semantics"
    - "a survey of paraphrasing and textual entailment methods"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "expressing implicit semantic relations without supervision"

Connected through hyperedge HE-6 (induced by source paper "domain and function a dual space model of semantic relations and compositions"):
  with members:
    - "experimenting with transitive verbs in a discocat"
    - "from frequency to meaning vector space models of semantics"
    - "bagpack a general framework to represent semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "expressing implicit semantic relations without supervision"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "combining independent modules to solve multiple choice synonym and analogy problems"

Connected through hyperedge HE-7 (induced by source paper "analogy perception applied to seven tests of word comprehension"):
  with members:
    - "roget s thesaurus and semantic similarity"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "measuring semantic similarity by latent relational analysis"
    - "combining independent modules to solve multiple choice synonym and analogy problems"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-8 (induced by source paper "from frequency to meaning vector space models of semantics"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "roget s thesaurus and semantic similarity"
    - "a uniform approach to analogies synonyms antonyms and associations"
    - "empirical evaluation of four tensor decomposition algorithms"
    - "similarity of semantic relations"
    - "corpus based learning of analogies and semantic relations"
    - "measuring semantic similarity by latent relational analysis"
    - "combining independent modules to solve multiple choice synonym and analogy problems"

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)