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: "measuring thematic fit with distributional feature overlap"
  Abstract: In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.

Connected through hyperedge HE-1 (induced by source paper "multiplex word embeddings for selectional preference acquisition"):
  with members:
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "knowledge aware pronoun coreference resolution"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamics of core of language vocabulary"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (induced by source paper "enabling robots to understand incomplete natural language instructions using commonsense reasoning"):
  with members:
    - "improving grounded natural language understanding through human robot dialog"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "a simple method for commonsense reasoning"
    - "attention based natural language grounding by navigating virtual environment"
    - "deep contextualized word representations"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"

Connected through hyperedge HE-3 (induced by source paper "a structured distributional model of sentence meaning and processing"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "question answering with grammatically interpretable representations"
    - "the mechanism of additive composition"
    - "is structure necessary for modeling argument expectations in distributional semantics"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representing verbs with rich contexts an evaluation on verb similarity"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-4 (induced by source paper "sp 10k a large scale evaluation set for selectional preference acquisition"):
  with members:
    - "higher order coreference resolution with coarse to fine inference"
    - "revisiting selectional preferences for coreference resolution"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "inducing a semantically annotated lexicon via em based clustering"

Connected through hyperedge HE-5 (induced by source paper "a rank based similarity metric for word embeddings"):
  with members:
    - "automated generation of multilingual clusters for the evaluation of distributed representations"
    - "what a nerd beating students and vector cosine in the esl and toefl datasets"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

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)