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: "approximation aware dependency parsing by belief propagation"
  Abstract: We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O(n^3) runtime. It outputs the parse with maximum expected recall -- but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as Stoyanov et al. (2011) and Domke (2010) did for loopy CRFs. The resulting trained parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.

Connected through hyperedge HE-1 (induced by source paper "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

Connected through hyperedge HE-2 (induced by source paper "second order semantic dependency parsing with end to end neural networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning joint semantic parsers from disjoint data"
    - "simpler but more accurate semantic dependency parsing"
    - "structured attentions for visual question answering"
    - "deep multitask learning for semantic dependency parsing"
    - "deep biaffine attention for neural dependency parsing"
    - "conditional random fields as recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "cavs a vertex centric programming interface for dynamic neural networks"):
  with members:
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "deep learning with dynamic computation graphs"
    - "on the fly operation batching in dynamic computation graphs"
    - "semantic object parsing with graph lstm"
    - "efficient softmax approximation for gpus"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "tensorflow a system for large scale machine learning"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"

Connected through hyperedge HE-4 (induced by source paper "structured attention networks"):
  with members:
    - "textual entailment with structured attentions and composition"
    - "online segment to segment neural transduction"
    - "enhanced lstm for natural language inference"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "neural architectures for named entity recognition"
    - "neural tree indexers for text understanding"
    - "the neural noisy channel"
    - "end to end memory networks"

Connected through hyperedge HE-5 (induced by source paper "a continuous relaxation of beam search for end to end training of neural sequence models"):
  with members:
    - "neural machine translation with gumbel greedy decoding"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "differentiable scheduled sampling for credit assignment"
    - "six challenges for neural machine translation"
    - "sequence to sequence learning as beam search optimization"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "differentiable scheduled sampling for credit assignment"):
  with members:
    - "towards decoding as continuous optimization in neural machine translation"
    - "sequence to sequence learning as beam search optimization"
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"
    - "a neural attention model for abstractive sentence summarization"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning to transduce with unbounded memory"

Connected through hyperedge HE-7 (induced by source paper "tensorlog deep learning meets probabilistic dbs"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "harnessing deep neural networks with logic rules"
    - "key value memory networks for directly reading documents"
    - "learning to compose neural networks for question answering"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "lifted relational neural networks"
    - "probabilistic similarity logic"

Connected through hyperedge HE-8 (induced by source paper "tensorlog a differentiable deductive database"):
  with members:
    - "learning to compose neural networks for question answering"

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)