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: "antnet distributed stigmergetic control for communications networks"
  Abstract: This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

Connected through hyperedge HE-1 (induced by source paper "stigmergic independent reinforcement learning for multi agent collaboration"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "prioritized experience replay"
    - "mobile crowd sensing and computing when participatory sensing meets participatory social media"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-2 (induced by source paper "socially aware congestion control in ad hoc networks current status and the way forward"):
  with members:
    - "privacy preserving schemes for ad hoc social networks a survey"
    - "smartphone based vehicle telematics a ten year anniversary"
    - "socially aware networking a survey"

Connected through hyperedge HE-3 (induced by source paper "artificial ant species on solving optimization problems"):
  with members:
    - "distributed pharaoh system for network routing"
    - "new results of ant algorithms for the linear ordering problem"
    - "sensitive ants in solving the generalized vehicle routing problem"

Connected through hyperedge HE-4 (induced by source paper "multiple random walks to uncover short paths in power law networks"):
  with members:
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

Connected through hyperedge HE-5 (induced by source paper "multiple ant bee colony optimization for load balancing in packet switched networks"):
  with members:
    - "a novel solution to the dynamic routing and wavelength assignment problem in transparent optical networks"

Connected through hyperedge HE-6 (induced by source paper "nature inspired interconnects for self assembled large scale network on chip designs"):
  with members:
    - "searching complex networks efficiently with minimal information"

Connected through hyperedge HE-7 (induced by source paper "cooperative information sharing to improve distributed learning in multi agent systems"):
  with members:
    - "reinforcement learning for adaptive routing"
    - "decentralized supply chain formation a market protocol and competitive equilibrium analysis"
    - "towards flexible teamwork"

Connected through hyperedge HE-8 (induced by source paper "a model based approach to reachability routing"):
  with members:
    - "reinforcement learning a survey"

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.AI(Artificial Intelligence)