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: "deep graph library towards efficient and scalable deep learning on graphs"
  Abstract: Accelerating research in the emerging field of deep graph learning requires new tools. Such systems should support graph as the core abstraction and take care to maintain both forward (i.e. supporting new research ideas) and backward (i.e. integration with existing components) compatibility. In this paper, we present Deep Graph Library (DGL). DGL enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks. DGL carefully handles the sparse and irregular graph structure, deals with graphs big and small which may change dynamically, fuses operations, and performs auto-batching, all to take advantages of modern hardware. DGL has been tested on a variety of models, including but not limited to the popular Graph Neural Networks (GNN) and its variants, with promising speed, memory footprint and scalability.

Connected through hyperedge HE-1 (induced by source paper "visual semantic graph attention networks for human object interaction detection"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "pose aware multi level feature network for human object interaction detection"
    - "detecting human object interactions via functional generalization"
    - "relational action forecasting"
    - "a comprehensive survey on graph neural networks"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "ican instance centric attention network for human object interaction detection"
    - "no frills human object interaction detection factorization layout encodings and training techniques"

Connected through hyperedge HE-2 (induced by source paper "towards graph representation learning in emergent communication"):
  with members:
    - "capacity bandwidth and compositionality in emergent language learning"
    - "egg a toolkit for research on emergence of language in games"
    - "pytorch an imperative style high performance deep learning library"
    - "how agents see things on visual representations in an emergent language game"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-3 (induced by source paper "supervised learning on relational databases with graph neural networks"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "a comprehensive survey on graph neural networks"
    - "tabnet attentive interpretable tabular learning"
    - "relational pooling for graph representations"
    - "neural feature learning from relational database"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "learning distributed representations of graphs with geo2dr"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "fast graph representation learning with pytorch geometric"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"

Connected through hyperedge HE-5 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "fast and deep graph neural networks"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-6 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "sequence labeling a practical approach"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional self attention network"

Connected through hyperedge HE-7 (induced by source paper "bp transformer modelling long range context via binary partitioning"):
  with members:
    - "hierarchical transformers for multi document summarization"
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"

Connected through hyperedge HE-8 (induced by source paper "dropedge towards deep graph convolutional networks on node classification"):
  with members:
    - "on asymptotic behaviors of graph cnns from dynamical systems perspective"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "adaptive graph convolutional neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "large scale learnable graph convolutional 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)