human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "universal self attention network for graph classification"):
  Abstract: Existing graph neural network-based models have mainly been biased towards a supervised training setting; and they often share the common limitations in exploiting potential dependencies among nodes. To this end, we present U2GNN, a novel embedding model leveraging on the strength of the recently introduced universal self-attention network (Dehghaniet al., 2019), to learn low-dimensional embeddings of graphs which can be used for graph classification. In particular, given an input graph, U2GNN first applies a self-attention computation, which is then followed by a recurrent transition to iteratively memorize its attention on vector representations of each node and its neighbors across each iteration. Thus, U2GNN can address the limitations in the existing models to produce plausible node embeddings whose sum is the final embedding of the whole graph. Experimental results in both supervised and unsupervised training settings show that our U2GNN produces new state-of-the-art performances on a range of well-known benchmark datasets for the graph classification task. To the best of our knowledge, this is the first work showing that a unsupervised model can significantly work better than supervised models by a large margin.

Includes members:
  Member "are powerful graph neural nets necessary a dissection on graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmarking graph neural networks")
  Member "discriminative structural graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what graph neural networks cannot learn depth vs width")
  Member "graph neural networks a review of methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "relational inductive biases deep learning and graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "anonymous walk embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "network representation learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network")

Question: Please tell me which class the source paper 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)