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: "recurrent relational networks"
  Abstract: This paper is concerned with learning to solve tasks that require a chain of interdependent steps of relational inference, like answering complex questions about the relationships between objects, or solving puzzles where the smaller elements of a solution mutually constrain each other. We introduce the recurrent relational network, a general purpose module that operates on a graph representation of objects. As a generalization of Santoro et al. [2017]'s relational network, it can augment any neural network model with the capacity to do many-step relational reasoning. We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks. As bAbI is not particularly challenging from a relational reasoning point of view, we introduce Pretty-CLEVR, a new diagnostic dataset for relational reasoning. In the Pretty-CLEVR set-up, we can vary the question to control for the number of relational reasoning steps that are required to obtain the answer. Using Pretty-CLEVR, we probe the limitations of multi-layer perceptrons, relational and recurrent relational networks. Finally, we show how recurrent relational networks can learn to solve Sudoku puzzles from supervised training data, a challenging task requiring upwards of 64 steps of relational reasoning. We achieve state-of-the-art results amongst comparable methods by solving 96.6% of the hardest Sudoku puzzles.

Connected through hyperedge HE-1 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "neural jump stochastic differential equations"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"

Connected through hyperedge HE-2 (induced by source paper "relational action forecasting"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "recurrent relational networks for complex relational reasoning"
    - "relational recurrent neural networks"
    - "actor centric relation network"
    - "relational inductive biases deep learning and graph networks"
    - "object level visual reasoning in videos"
    - "predicting deeper into the future of semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "neural consciousness flow"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "relational deep reinforcement learning"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (induced by source paper "what can neural networks reason about"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "learning to make analogies by contrasting abstract relational structure"
    - "raven a dataset for relational and analogical visual reasoning"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the turing completeness of modern neural network architectures"

Connected through hyperedge HE-5 (induced by source paper "dynamically pruned message passing networks for large scale knowledge graph reasoning"):
  with members:
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "how powerful are graph neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "non local neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep reasoning networks thinking fast and slow"):
  with members:
    - "pdp a general neural framework for learning constraint satisfaction solvers"
    - "a pca like autoencoder"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "deep generative models with learnable knowledge constraints"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "recurrent relational networks for complex relational reasoning"
    - "combinatorial optimization with graph convolutional networks and guided tree search"

Connected through hyperedge HE-7 (induced by source paper "controlling neural networks via energy dissipation"):
  with members:
    - "linear inequality constraints for neural network activations"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "homogeneous linear inequality constraints for neural network activations"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "scale recurrent network for deep image deblurring"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep image prior"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-8 (induced by source paper "discrete and continuous deep residual learning over graphs"):
  with members:
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "a comprehensive survey on graph neural networks"
    - "group normalization"
    - "learning a sat solver from single bit supervision"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

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)