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: "how to construct deep recurrent neural networks"
  Abstract: In this paper, we explore different ways to extend a recurrent neural network (RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN (Schmidhuber, 1992; El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs.

Connected through hyperedge HE-1 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "stcn stochastic temporal convolutional networks"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "recurrent convolutions for causal 3d cnns"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "convolutional image captioning"

Connected through hyperedge HE-2 (induced by source paper "modernizing historical documents a user study"):
  with members:
    - "a call for clarity in reporting bleu scores"
    - "opennmt open source toolkit for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving historical spelling normalization with bi directional lstms and multi task learning"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints"):
  with members:
    - "interpreting and evaluating neural network robustness"
    - "representing formal languages a comparison between finite automata and recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "qualitatively characterizing neural network optimization problems"

Connected through hyperedge HE-4 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-5 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning deep transformer models for machine translation"
    - "pre trained language model representations for language generation"

Connected through hyperedge HE-6 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "chirality nets for human pose regression"):
  with members:
    - "pic permutation invariant critic for multi agent deep reinforcement learning"
    - "unsupervised learning of object structure and dynamics from videos"
    - "on the convergence of adam and beyond"
    - "ordinal depth supervision for 3d human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "action agnostic human pose forecasting"

Connected through hyperedge HE-8 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

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.NE(Neural and Evolutionary Computing)