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: "bleu is not suitable for the evaluation of text simplification"
  Abstract: BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.

Connected through hyperedge HE-1 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "delivering cognitive behavioral therapy using a conversational socialrobot"
    - "huggingface s transformers state of the art natural language processing"
    - "affect driven dialog generation"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "controllable sentence simplification"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reference less quality estimation of text simplification systems"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "easse easier automatic sentence simplification evaluation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "unsupervised sentence compression using denoising auto encoders"
    - "sentence simplification with memory augmented neural networks"
    - "dynamic multi level multi task learning for sentence simplification"

Connected through hyperedge HE-4 (induced by source paper "transforming complex sentences into a semantic hierarchy"):
  with members:
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "simple and effective text simplification using semantic and neural methods"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "editnts an neural programmer interpreter model for sentence simplification through explicit editing"):
  with members:
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "a constrained sequence to sequence neural model for sentence simplification"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "deep residual learning for image recognition"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "minwikisplit a sentence splitting corpus with minimal propositions"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "neural network acceptability judgments"
    - "learning to split and rephrase from wikipedia edit history"
    - "split and rephrase"

Connected through hyperedge HE-7 (induced by source paper "easse easier automatic sentence simplification evaluation"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "semantic structural evaluation for text simplification"
    - "a call for clarity in reporting bleu scores"
    - "a transition based directed acyclic graph parser for ucca"
    - "attention is all you need"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "towards neural language evaluators"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "roberta a robustly optimized bert pretraining approach"
    - "attention is all you need"
    - "a broad coverage challenge corpus for sentence understanding through inference"

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)