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: "towards neural network based reasoning"
  Abstract: We propose Neural Reasoner, a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks. Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions. Despite the model complexity, Neural Reasoner can still be trained effectively in an end-to-end manner. Our empirical studies show that Neural Reasoner can outperform existing neural reasoning systems with remarkable margins on two difficult artificial tasks (Positional Reasoning and Path Finding) proposed in [8]. For example, it improves the accuracy on Path Finding(10K) from 33.4% [6] to over 98%.

Connected through hyperedge HE-1 (induced by source paper "multi relational question answering from narratives machine reading and reasoning in simulated worlds"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the narrativeqa reading comprehension challenge"
    - "answering complex questions using open information extraction"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-2 (induced by source paper "nlprolog reasoning with weak unification for question answering in natural language"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-3 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "actively seeking and learning from live data"
    - "hyperbolic neural networks"
    - "compositional attention networks for machine reasoning"
    - "deep learning a critical appraisal"
    - "visual question answering as a meta learning task"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "evolutionary algorithm for sinhala to english translation"):
  with members:
    - "language modeling through long term memory network"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "reasoning over rdf knowledge bases using deep learning"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "ontology reasoning with deep neural networks"
    - "disentangling language and knowledge in task oriented dialogs"
    - "learning to compute word embeddings on the fly"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-7 (induced by source paper "improved dynamic memory network for dialogue act classification with adversarial training"):
  with members:
    - "improving automatic source code summarization via deep reinforcement learning"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "dialogue act recognition via crf attentive structured network"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "data noising as smoothing in neural network language models"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "image captioning with semantic attention"
    - "a latent variable recurrent neural network for discourse relation language models"

Connected through hyperedge HE-8 (induced by source paper "exploiting sentence embedding for medical question answering"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "medical exam question answering with large scale reading comprehension"
    - "a structured self attentive sentence embedding"
    - "reading wikipedia to answer open domain questions"
    - "long short term memory networks for machine reading"
    - "iterative alternating neural attention for machine reading"

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.AI(Artificial Intelligence)