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 "embedding syntax and semantics of prepositions via tensor decomposition"):
  Abstract: Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences. Not surprisingly, they pose well-known difficulties in automatic processing of sentences (prepositional attachment ambiguities and idiosyncratic uses in phrases). Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe). In addition, recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations. In this paper we use word-triple counts (one of the triples being a preposition) to capture a preposition's interaction with its attachment and complement. We then derive preposition embeddings via tensor decomposition on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing phrasal verbs. Furthermore, our preposition embeddings are used as simple features in two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve results comparable to or better than the state-of-the-art on multiple standardized datasets.

Includes members:
  Member "ontology aware token embeddings for prepositional phrase attachment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using multi sense vector embeddings for reverse dictionaries")
  Member "from language to programs bridging reinforcement learning and maximum marginal likelihood":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "why build an assistant in minecraft")
  Member "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "embedding lexical features via low rank tensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge base relation detection via multi view matching")
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parsing as pretraining")
  Member "tensor factorization via matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use")
  Member "autoextend extending word embeddings to embeddings for synsets and lexemes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")

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)