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 for complex relational reasoning"
  Abstract: Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models. Relational networks, introduced by Santoro et al. (2017), add the capacity for relational reasoning to deep neural networks, but are limited in the complexity of the reasoning tasks they can address. We introduce recurrent relational networks which increase the suite of solvable tasks to those that require an order of magnitude more steps of relational reasoning. We use recurrent relational networks to solve Sudoku puzzles and achieve state-of-the-art results by solving 96.6% of the hardest Sudoku puzzles, where relational networks fail to solve any. We also apply our model to the BaBi textual QA dataset solving 19/20 tasks which is competitive with state-of-the-art sparse differentiable neural computers. The recurrent relational network is a general purpose module that can augment any neural network model with the capacity to do many-step relational reasoning.

Connected through hyperedge HE-1 (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"
    - "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"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (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"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "end to end learning for the deep multivariate probit model"

Connected through hyperedge HE-3 (induced by source paper "typed graph networks"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "multitask learning on graph neural networks learning multiple graph centrality measures with a unified network"
    - "relational inductive biases deep learning and graph networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "graph colouring meets deep learning effective graph neural network models for combinatorial problems"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "relational inductive biases deep learning and graph networks"
    - "recurrent relational networks"
    - "neural message passing for quantum chemistry"
    - "combining the k cnf and xor phase transitions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "neural networks for relational data"):
  with members:
    - "can neural networks understand logical entailment"
    - "harnessing deep neural networks with logic rules"
    - "column networks for collective classification"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "learning convolutional neural networks for graphs"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "deepwalk online learning of social representations"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-6 (induced by source paper "object based reasoning in vqa"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "neural collaborative filtering"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "detecting visual relationships with deep relational networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "one model to learn them all"

Connected through hyperedge HE-7 (induced by source paper "multitask learning on graph neural networks learning multiple graph centrality measures with a unified network"):
  with members:
    - "machine learning in network centrality measures tutorial and outlook"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "learning a sat solver from single bit supervision"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep learning on graphs a survey"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "computing vertex centrality measures in massive real networks with a neural learning model"

Connected through hyperedge HE-8 (induced by source paper "pose based two stream relational networks for action recognition in videos"):
  with members:
    - "attention is all you need"
    - "towards accurate multi person pose estimation in the wild"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "pose for action action for pose"
    - "temporal segment networks towards good practices for deep action recognition"

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)