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: "embedding lexical features via low rank tensors"
  Abstract: Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers of features, which can lead to over-fitting. We present a new model that represents complex lexical features---comprised of parts for words, contextual information and labels---in a tensor that captures conjunction information among these parts. We apply low-rank tensor approximations to the corresponding parameter tensors to reduce the parameter space and improve prediction speed. Furthermore, we investigate two methods for handling features that include $n$-grams of mixed lengths. Our model achieves state-of-the-art results on tasks in relation extraction, PP-attachment, and preposition disambiguation.

Connected through hyperedge HE-1 (induced by source paper "knowledge base relation detection via multi view matching"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "bilateral multi perspective matching for natural language sentences"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "text understanding with the attention sum reader network"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "gated attention readers for text comprehension"
    - "question answering on freebase via relation extraction and textual evidence"

Connected through hyperedge HE-2 (induced by source paper "embedding syntax and semantics of prepositions via tensor decomposition"):
  with members:
    - "ontology aware token embeddings for prepositional phrase attachment"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "tensor factorization via matrix factorization"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "polyglot distributed word representations for multilingual nlp"

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)