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: "deep enhanced representation for implicit discourse relation recognition"
  Abstract: Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48% in 11-way and $F_1$ score greater than 50% in 4-way classifications for the first time according to our best knowledge.

Connected through hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "cross lingual distillation for text classification"
    - "aspect augmented adversarial networks for domain adaptation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "from source to target and back symmetric bi directional adaptive gan"

Connected through hyperedge HE-2 (induced by source paper "subword elmo"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-3 (induced by source paper "memorizing all for implicit discourse relation recognition"):
  with members:
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "implicit discourse relation classification via multi task neural networks"
    - "deep biaffine attention for neural dependency parsing"
    - "language modeling with gated convolutional networks"
    - "key value memory networks for directly reading documents"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"

Connected through hyperedge HE-4 (induced by source paper "implicit discourse relation identification for open domain dialogues"):
  with members:
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "talking to myself self dialogues as data for conversational agents"
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "conversational ai the science behind the alexa prize"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"

Connected through hyperedge HE-5 (induced by source paper "zero shot transfer for implicit discourse relation classification"):
  with members:
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "dissent sentence representation learning from explicit discourse relations"

Connected through hyperedge HE-6 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"
    - "the interplay of semantics and morphology in word embeddings"

Connected through hyperedge HE-8 (induced by source paper "modeling multi turn conversation with deep utterance aggregation"):
  with members:
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "fast and accurate neural word segmentation for chinese"
    - "a network based end to end trainable task oriented dialogue system"

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)