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: "path based vs distributional information in recognizing lexical semantic relations"
  Abstract: Recognizing various semantic relations between terms is beneficial for many NLP tasks. While path-based and distributional information sources are considered complementary for this task, the superior results the latter showed recently suggested that the former's contribution might have become obsolete. We follow the recent success of an integrated neural method for hypernymy detection (Shwartz et al., 2016) and extend it to recognize multiple relations. The empirical results show that this method is effective in the multiclass setting as well. We further show that the path-based information source always contributes to the classification, and analyze the cases in which it mostly complements the distributional information.

Connected through hyperedge HE-1 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification"):
  with members:
    - "relational word embeddings"
    - "kg bert bert for knowledge graph completion"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"

Connected through hyperedge HE-2 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "scoring lexical entailment with a supervised directional similarity network"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "term definitions help hypernymy detection"

Connected through hyperedge HE-3 (induced by source paper "assessing the lexico semantic relational knowledge captured by word and concept embeddings"):
  with members:
    - "not just about size a study on the role of distributed word representations in the analysis of scientific publications"
    - "a decomposable attention model for natural language inference"
    - "swivel improving embeddings by noticing what s missing"
    - "enriching word vectors with subword information"
    - "proje embedding projection for knowledge graph completion"
    - "visualizing and understanding neural models in nlp"
    - "experiments with three approaches to recognizing lexical entailment"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "pair2vec compositional word pair embeddings for cross sentence inference"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-5 (induced by source paper "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"):
  with members:
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "distributed representations of words and phrases and their compositionality"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-6 (induced by source paper "cogalex v shared task lexnet integrated path based and distributional method for the identification of semantic relations"):
  with members:
    - "testing apsyn against vector cosine on similarity estimation"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "measures of distributional similarity"

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)