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: "sp 10k a large scale evaluation set for selectional preference acquisition"
  Abstract: Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks. To provide a better evaluation method for SP models, we introduce SP-10K, a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. Three representative SP acquisition methods based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the importance of our dataset, we investigate the relationship between SP-10K and the commonsense knowledge in ConceptNet5 and show the potential of using SP to represent the commonsense knowledge. We also use the Winograd Schema Challenge to prove that the proposed new SP relations are essential for the hard pronoun coreference resolution problem.

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"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "measuring thematic fit with distributional feature overlap"
    - "dynamics of core of language vocabulary"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "aser a large scale eventuality knowledge graph"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "a knowledge hunting framework for common sense reasoning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)