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: "theoretical investigation of composite neural network"
  Abstract: A composite neural network is a rooted directed acyclic graph combining a set of pre-trained and non-instantiated neural network models. A pre-trained neural network model is well-crafted for a specific task and with instantiated weights. is generally well trained, targeted to approximate a specific function. Despite a general belief that a composite neural network may perform better than a single component, the overall performance characteristics are not clear. In this work, we prove that there exist parameters such that a composite neural network performs better than any of its pre-trained components with a high probability bound.

Connected through hyperedge HE-1 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network"):
  with members:
    - "behavioral malware classification using convolutional recurrent neural networks"
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "robust neural malware detection models for emulation sequence learning"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "virtual machine introspection based malware behavior profiling and family grouping"
    - "attention is all you need"
    - "dynamic analysis of executables to detect and characterize malware"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "composite neural network theory and application to pm2 5 prediction"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "autoencoder by forest"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "issues in stacked generalization"

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)