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: "the neural network pushdown automation model stack and learning simulations"
  Abstract: In order for neural networks to learn complex languages or grammars, they must have sufficient computational power or resources to recognize or generate such languages. Though many approaches have been discussed, one ob- vious approach to enhancing the processing power of a recurrent neural network is to couple it with an external stack memory - in effect creating a neural network pushdown automata (NNPDA). This paper discusses in detail this NNPDA - its construction, how it can be trained and how useful symbolic information can be extracted from the trained network. #R##N#In order to couple the external stack to the neural network, an optimization method is developed which uses an error function that connects the learning of the state automaton of the neural network to the learning of the operation of the external stack. To minimize the error function using gradient descent learning, an analog stack is designed such that the action and storage of information in the stack are continuous. One interpretation of a continuous stack is the probabilistic storage of and action on data. After training on sample strings of an unknown source grammar, a quantization procedure extracts from the analog stack and neural network a discrete pushdown automata (PDA). Simulations show that in learning deterministic context-free grammars - the balanced parenthesis language, 1*n0*n, and the deterministic Palindrome - the extracted PDA is correct in the sense that it can correctly recognize unseen strings of arbitrary length. In addition, the extracted PDAs can be shown to be identical or equivalent to the PDAs of the source grammars which were used to generate the training strings.

Connected through hyperedge HE-1 (induced by source paper "differentiable grammars for videos"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "temporal gaussian mixture layer for videos"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "recurrent neural network grammars"

Connected through hyperedge HE-2 (induced by source paper "memory augmented recursive neural networks"):
  with members:
    - "can neural networks understand logical entailment"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "graph memory networks for molecular activity prediction"
    - "combining symbolic expressions and black box function evaluations in neural programs"
    - "making neural programming architectures generalize via recursion"
    - "tree memory networks for modelling long term temporal dependencies"
    - "learning continuous semantic representations of symbolic expressions"
    - "learning to transduce with unbounded memory"

Connected through hyperedge HE-3 (induced by source paper "learning differentiable grammars for continuous data"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predictive corrective networks for action detection"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "recurrent neural network grammars"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "asynchronous temporal fields for action recognition"

Connected through hyperedge HE-4 (induced by source paper "ordered neurons integrating tree structures into recurrent neural networks"):
  with members:
    - "the emergence of number and syntax units in lstm language models"
    - "on tree based neural sentence modeling"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "grammar induction with neural language models an unusual replication"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-5 (induced by source paper "a survey on analog models of computation"):
  with members:
    - "real time computability of real numbers by chemical reaction networks"
    - "robustness of expressivity in chemical reaction networks"
    - "efficient analog circuits for boolean satisfiability"
    - "axiomatizing analog algorithms"
    - "homonym population protocols"
    - "physical computation p poly and p log"
    - "a uniform substitution calculus for differential dynamic logic"
    - "on recurrent reachability for continuous linear dynamical systems"

Connected through hyperedge HE-6 (induced by source paper "a taxonomy for neural memory networks"):
  with members:
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "learning to transduce with unbounded memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural turing machines"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning explanatory rules from noisy data"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "differentiable functional program interpreters"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "terpret a probabilistic programming language for program induction"
    - "neural programmer inducing latent programs with gradient descent"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-8 (induced by source paper "do latent tree learning models identify meaningful structure in sentences"):
  with members:
    - "learning structured text representations"
    - "what do neural machine translation models learn about morphology"
    - "tree to sequence attentional neural machine translation"
    - "neural semantic encoders"
    - "neural tree indexers for text understanding"
    - "hierarchical multiscale recurrent neural networks"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "natural language inference by tree based convolution and heuristic matching"

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)