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: "lexicosyntactic inference in neural models"
  Abstract: We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.

Connected through hyperedge HE-1 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

Connected through hyperedge HE-2 (induced by source paper "automatically neutralizing subjective bias in text"):
  with members:
    - "towards controlled transformation of sentiment in sentences"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating style transfer for text"
    - "identifying and reducing gender bias in word level language models"
    - "understanding undesirable word embedding associations"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "delete retrieve generate a simple approach to sentiment and style transfer"

Connected through hyperedge HE-3 (induced by source paper "fine grained temporal relation extraction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "decomposing generalization models of generic habitual and episodic statements"
    - "a structured learning approach to temporal relation extraction"
    - "deep contextualized word representations"
    - "neural models of factuality"
    - "temporal information extraction by predicting relative time lines"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "the lexical and grammatical sources of neg raising inferences"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

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)