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: "bidirectional recursive neural networks for token level labeling with structure"
  Abstract: Recently, deep architectures, such as recurrent and recursive neural networks have been successfully applied to various natural language processing tasks. Inspired by bidirectional recurrent neural networks which use representations that summarize the past and future around an instance, we propose a novel architecture that aims to capture the structural information around an input, and use it to label instances. We apply our method to the task of opinion expression extraction, where we employ the binary parse tree of a sentence as the structure, and word vector representations as the initial representation of a single token. We conduct preliminary experiments to investigate its performance and compare it to the sequential approach.

Connected through hyperedge HE-1 (induced by source paper "a framework for end to end learning on semantic tree structured data"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "learning to play general video games via an object embedding network"
    - "tree to tree neural networks for program translation"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "structural attention neural networks for improved sentiment analysis"
    - "abstract syntax networks for code generation and semantic parsing"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "improving aspect term extraction with bidirectional dependency tree representation"):
  with members:
    - "lifelong learning crf for supervised aspect extraction"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi task memory networks for category specific aspect and opinion terms co extraction"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional tree structured lstm with head lexicalization"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-3 (induced by source paper "structural attention neural networks for improved sentiment analysis"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "ask me anything dynamic memory networks for natural language processing"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "twitter mood predicts the stock market"
    - "unsupervised learning of semantic orientation from a hundred billion word corpus"

Connected through hyperedge HE-4 (induced by source paper "neuro symbolic program synthesis"):
  with members:
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "neural programmer inducing latent programs with gradient descent"
    - "neural programmer interpreters"
    - "neural turing machines"
    - "grammar as a foreign language"

Connected through hyperedge HE-5 (induced by source paper "when are tree structures necessary for deep learning of representations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree structured composition in neural networks without tree structured architectures"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"
    - "recursive neural networks can learn logical semantics"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

Connected through hyperedge HE-6 (induced by source paper "feature weight tuning for recursive neural networks"):
  with members:
    - "distributed representations of sentences and documents"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "catching the drift probabilistic content models with applications to generation and summarization"
    - "thumbs up sentiment classification using machine learning techniques"

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.LG(Machine Learning)