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: "machine translation evaluation meets community question answering"
  Abstract: We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a pairwise neural network (NN) architecture, which incorporates MTE features, as well as rich syntactic and semantic embeddings, and which efficiently models complex non-linear interactions. The evaluation results show state-of-the-art performance, with sizeable contribution from both the MTE features and from the pairwise NN architecture.

Connected through hyperedge HE-1 (induced by source paper "a joint model for multimodal document quality assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "deep paper gestalt"
    - "automatic academic paper rating based on modularized hierarchical convolutional neural network"
    - "skipflow incorporating neural coherence features for end to end automatic text scoring"
    - "automatic text scoring using neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "semeval 2017 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "a decomposable attention model for natural language inference"
    - "semi supervised question retrieval with gated convolutions"

Connected through hyperedge HE-3 (induced by source paper "semeval 2016 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "answer sequence learning with neural networks for answer selection in community question answering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "pairwise neural machine translation evaluation"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "building chatbots from forum data model selection using question answering metrics"):
  with members:
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "enriching word vectors with subword information"
    - "a survey of available corpora for building data driven dialogue systems"

Connected through hyperedge HE-6 (induced by source paper "cross language learning with adversarial neural networks application to community question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "learning to translate for multilingual question answering"
    - "cross lingual models of word embeddings an empirical comparison"
    - "unrolled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "machine translation evaluation with neural networks"):
  with members:
    - "discotk using discourse structure for machine translation evaluation"
    - "pairwise neural machine translation evaluation"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "multitask learning with deep neural networks for community question answering"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)