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: "learning semantically and additively compositional distributional representations"
  Abstract: This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS). We show theoretical evidence that the vector compositions in our model conform to the logic of DCS. Experimentally, we show that vector-based composition brings a strong ability to calculate similar phrases as similar vectors, achieving near state-of-the-art on a wide range of phrase similarity tasks and relation classification; meanwhile, DCS can guide building vectors for structured queries that can be directly executed. We evaluate this utility on sentence completion task and report a new state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-2 (induced by source paper "interpretable and compositional relation learning by joint training with an autoencoder"):
  with members:
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "knowledge base completion baselines strike back"
    - "modeling large scale structured relationships with shared memory for knowledge base completion"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "proje embedding projection for knowledge graph completion"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"
    - "traversing knowledge graphs in vector space"
    - "compositional vector space models for knowledge base completion"

Connected through hyperedge HE-3 (induced by source paper "acquisition of phrase correspondences using natural deduction proofs"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a ccg parsing with a supertag and dependency factored model"
    - "determining semantic textual similarity using natural deduction proofs"
    - "task specific attentive pooling of phrase alignments contributes to sentence matching"
    - "representing meaning with a combination of logical and distributional models"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-4 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "rhetorical relations for information retrieval"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "part of speech based term weighting for information retrieval"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"

Connected through hyperedge HE-5 (induced by source paper "the mechanism of additive composition"):
  with members:
    - "composing distributed representations of relational patterns"
    - "a latent variable model approach to pmi based word embeddings"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "word emdeddings through hellinger pca"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "a context theoretic framework for compositionality in distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-6 (induced by source paper "improving semantic composition with offset inference"):
  with members:
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "discriminative neural sentence modeling by tree based convolution"
    - "a study of entanglement in a categorical framework of natural language"
    - "distributed tree kernels"
    - "domain and function a dual space model of semantic relations and compositions"
    - "experimental support for a categorical compositional distributional model of meaning"
    - "concrete sentence spaces for compositional distributional models of meaning"

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)