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: "discriminative models for multi instance problems with tree structure"
  Abstract: Modeling network traffic is gaining importance in order to counter modern threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in model training phase. We propose a discriminative model that makes decisions based on all computer's traffic observed during predefined time window (5 minutes in our case). The model is trained on collected traffic samples over equally sized time window per large number of computers, where the only labels needed are human verdicts about the computer as a whole (presumed infected vs. presumed clean). As part of training the model itself recognizes discriminative patterns in traffic targeted to individual servers and constructs the final high-level classifier on top of them. We show the classifier to perform with very high precision, while the learned traffic patterns can be interpreted as Indicators of Compromise. In the following we implement the discriminative model as a neural network with special structure reflecting two stacked multi-instance problems. The main advantages of the proposed configuration include not only improved accuracy and ability to learn from gross labels, but also automatic learning of server types (together with their detectors) which are typically visited by infected computers.

Connected through hyperedge HE-1 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning"):
  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"
    - "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"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "using neural network formalism to solve multiple instance problems"
    - "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-3 (induced by source paper "joint detection of malicious domains and infected clients"):
  with members:
    - "yes machine learning can be more secure a case study on android malware detection"
    - "convolutional sequence to sequence learning"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with deep relationship networks"
    - "learning multiple tasks with multilinear relationship networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "a simple neural network module for relational reasoning"
    - "using neural network formalism to solve multiple instance problems"
    - "distributed representations of words and phrases and their compositionality"

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.CR(Cryptography and Security)