human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  Abstract: We introduce HyperLex - a dataset and evaluation resource that quantifies the extent of of the semantic category membership and lexical entailment (LE) relation between 2,616 concept pairs. Cognitive psychology research has established that category/class membership, and hence LE, is computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research, and existing large-scale invetories of concept category membership (WordNet, DBPedia, etc.) treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgements with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.

Includes members:
  Member "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from explanations with neural execution tree")
  Member "morph fitting fine tuning word vector spaces with simple language specific rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attentive mimicking better word embeddings by attending to informative contexts")
  Member "baselines and test data for cross lingual inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension")
  Member "bilingual learning of multi sense embeddings with discrete autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a variational approach to weakly supervised document level multi aspect sentiment classification")
  Member "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "intrinsic subspace evaluation of word embedding representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings")
  Member "counter fitting word vectors to linguistic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")

Question: Please tell me which class the source paper 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)