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: "semantic sentence matching with densely connected recurrent and co attentive information"
  Abstract: Sentence matching is widely used in various natural language tasks such as natural language inference, paraphrase identification, and question answering. For these tasks, understanding logical and semantic relationship between two sentences is required but it is yet challenging. Although attention mechanism is useful to capture the semantic relationship and to properly align the elements of two sentences, previous methods of attention mechanism simply use a summation operation which does not retain original features enough. Inspired by DenseNet, a densely connected convolutional network, we propose a densely-connected co-attentive recurrent neural network, each layer of which uses concatenated information of attentive features as well as hidden features of all the preceding recurrent layers. It enables preserving the original and the co-attentive feature information from the bottommost word embedding layer to the uppermost recurrent layer. To alleviate the problem of an ever-increasing size of feature vectors due to dense concatenation operations, we also propose to use an autoencoder after dense concatenation. We evaluate our proposed architecture on highly competitive benchmark datasets related to sentence matching. Experimental results show that our architecture, which retains recurrent and attentive features, achieves state-of-the-art performances for most of the tasks.

Connected through hyperedge HE-1 (induced by source paper "utilizing bert intermediate layers for aspect based sentiment analysis and natural language inference"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "probing contextualized sentence representations with visual awareness"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "semantics aware bert for language understanding"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "glyce glyph vectors for chinese character representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-3 (induced by source paper "semantics aware bert for language understanding"):
  with members:
    - "limit bert linguistic informed multi task bert"
    - "dependency or span end to end uniform semantic role labeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "a lightweight recurrent network for sequence modeling"):
  with members:
    - "rational recurrences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "accelerating neural transformer via an average attention network"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "recurrent additive networks"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-5 (induced by source paper "simple but effective techniques to reduce biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "rubi reducing unimodal biases in visual question answering"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "probing neural network comprehension of natural language arguments"
    - "on adversarial removal of hypothesis only bias in natural language inference"

Connected through hyperedge HE-6 (induced by source paper "mrnn a multi resolution neural network with duplex attention for document retrieval in the context of question answering"):
  with members:
    - "a multi resolution word embedding for document retrieval from large unstructured knowledge bases"
    - "training a ranking function for open domain question answering"
    - "deep contextualized word representations"
    - "deep relevance ranking using enhanced document query interactions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text embeddings for retrieval from a large knowledge base"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting persona information for diverse generation of conversational responses"
    - "evaluating coherence in dialogue systems using entailment"
    - "a study of reinforcement learning for neural machine translation"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples with difficult common words for paraphrase identification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "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)