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: "using neural network formalism to solve multiple instance problems"
  Abstract: Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each object (sample) by a set (bag) of feature vectors (instances) of fixed length where knowledge about objects (e.g., class label) is available on bag level but not necessarily on instance level. Many standard tools including supervised classifiers have been already adapted to MIL setting since the problem got formalized in late nineties. In this work we propose a neural network (NN) based formalism that intuitively bridges the gap between MIL problem definition and the vast existing knowledge-base of standard models and classifiers. We show that the proposed NN formalism is effectively optimizable by a modified back-propagation algorithm and can reveal unknown patterns inside bags. Comparison to eight types of classifiers from the prior art on a set of 14 publicly available benchmark datasets confirms the advantages and accuracy of the proposed solution.

Connected through hyperedge HE-1 (induced by source paper "nested multiple instance learning in modelling of http network traffic"):
  with members:
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "predicting domain generation algorithms with long short term memory networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "julia a fresh approach to numerical computing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning with explicitly represented knowledge and variable state and action spaces"):
  with members:
    - "effective medical test suggestions using deep reinforcement learning"
    - "classification with costly features as a sequential decision making problem"
    - "discretizing continuous action space for on policy optimization"
    - "graph neural networks a review of methods and applications"
    - "relational deep reinforcement learning"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"

Connected through hyperedge HE-3 (induced by source paper "approximation capability of neural networks on spaces of probability measures and tree structured domains"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "deep sets"
    - "discriminative models for multi instance problems with tree structure"
    - "a simple neural network module for relational reasoning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "discriminative models for multi instance problems with tree structure"):
  with members:
    - "adam a method for stochastic optimization"
    - "learned norm pooling for deep feedforward and recurrent 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)