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 "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"):
  Abstract: Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). #R##N#In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non--linearly encoded in the embedding space, which questions the cosine-based, unsupervised, evaluation methods. All results and analysis scripts are available online.

Includes members:
  Member "expanding subjective lexicons for social media mining with embedding subspaces":
    also connected through other hyperedges:
      (none)
  Member "improving reliability of word similarity evaluation by redesigning annotation task and performance measure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "syntactic interchangeability in word embedding models")
  Member "creating causal embeddings for question answering with minimal supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "everything happens for a reason discovering the purpose of actions in procedural text")
  Member "linear algebraic structure of word senses with applications to polysemy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "non distributional word vector representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation of dense and sparse text representations")
  Member "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shoma at parseme shared task on automatic identification of vmwes neural multiword expression tagging with high generalisation")
  Member "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "a primer on neural network models for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t")

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)