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 attention model"
  Abstract: Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or sub-graphs. In the real-world, however, graphs can be both large and noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attentional processing for graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of "interesting" nodes. The model is equipped with an external memory component which allows it to integrate information gathered from different parts of the graph. We demonstrate the effectiveness of the model through various experiments.

Connected through hyperedge HE-1 (induced by source paper "collaborative graph walk for semi supervised multi label node classification"):
  with members:
    - "attention models in graphs a survey"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "lnemlc label network embeddings for multi label classifiation"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "vain attentional multi agent predictive modeling"
    - "distral robust multitask reinforcement learning"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "apk2vec semi supervised multi view representation learning for profiling android applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "anonymous walk embeddings"
    - "struc2vec learning node representations from structural identity"
    - "hash embeddings for efficient word representations"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "graph2vec learning distributed representations of graphs"
    - "contextual weisfeiler lehman graph kernel for malware detection"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "predicting graph categories from structural properties"):
  with members:
    - "network classification and categorization"
    - "role discovery in networks"
    - "graphlet decomposition framework algorithms and applications"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "subgraph matching kernels for attributed graphs"

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)