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: "just jump dynamic neighborhood aggregation in graph neural networks"
  Abstract: We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring embeddings of potentially differing locality. In order to avoid overfitting, we propose to control the channel-wise connections between input and output by making use of grouped linear projections. In a number of transductive node-classification experiments, we demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "fast graph representation learning with pytorch geometric"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "simplifying graph convolutional networks"
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a simple yet effective baseline for non attributed graph classification"

Connected through hyperedge HE-2 (induced by source paper "measuring and relieving the over smoothing problem for graph neural networks from the topological view"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "fisher bures adversary graph convolutional networks"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-3 (induced by source paper "graphsaint graph sampling based inductive learning method"):
  with members:
    - "graphact accelerating gcn training on cpu fpga heterogeneous platforms"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "accurate efficient and scalable graph embedding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-4 (induced by source paper "graph neural tangent kernel fusing graph neural networks with graph kernels"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what can neural networks reason about"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"

Connected through hyperedge HE-5 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "coherent comment generation for chinese articles with a graph to sequence model"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "demo net degree specific graph neural networks for node and graph classification"

Connected through hyperedge HE-6 (induced by source paper "pairnorm tackling oversmoothing in gnns"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "gmnn graph markov neural networks"
    - "simplifying graph convolutional networks"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

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.LG(Machine Learning)