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: "learning based constraint satisfaction with sensing restrictions"
  Abstract: In this paper we consider graph-coloring problems, an important subset of general constraint satisfaction problems that arise in wireless resource allocation. We constructively establish the existence of fully decentralized learning-based algorithms that are able to find a proper coloring even in the presence of strong sensing restrictions, in particular sensing asymmetry of the type encountered when hidden terminals are present. Our main analytic contribution is to establish sufficient conditions on the sensing behavior to ensure that the solvers find satisfying assignments with probability one. These conditions take the form of connectivity requirements on the induced sensing graph. These requirements are mild, and we demonstrate that they are commonly satisfied in wireless allocation tasks. We argue that our results are of considerable practical importance in view of the prevalence of both communication and sensing restrictions in wireless resource allocation problems. The class of algorithms analyzed here requires no message-passing whatsoever between wireless devices, and we show that they continue to perform well even when devices are only able to carry out constrained sensing of the surrounding radio environment.

Connected through hyperedge HE-1 (induced by source paper "spectrum intelligent radio technology development and future trends"):
  with members:
    - "mobile collaborative spectrum sensing for heterogeneous networks a bayesian machine learning approach"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "a survey on applications of model free strategy learning in cognitive wireless networks"

Connected through hyperedge HE-2 (induced by source paper "multi agent deep reinforcement learning based spectrum allocation for d2d underlay communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "a multi agent deep reinforcement learning based spectrum allocation framework for d2d underlay communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "continuous control with deep reinforcement learning"
    - "tact a transfer actor critic learning framework for energy saving in cellular radio access networks"

Connected through hyperedge HE-3 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "approximate best response dynamics in random interference games"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"
    - "active anomaly detection in heterogeneous processes"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "radio resource allocation for device to device underlay communication using hypergraph theory"):
  with members:
    - "resource management for device to device underlay communication"

Connected through hyperedge HE-5 (induced by source paper "distributed learning algorithms for spectrum sharing in spatial random access wireless networks"):
  with members:
    - "fast responsive decentralised graph colouring"
    - "distributed game theoretic optimization and management of multichannel aloha networks"
    - "distributed spectrum access with spatial reuse"
    - "cooperative game theory and the gaussian interference channel"
    - "bargaining over the interference channel"

Connected through hyperedge HE-6 (induced by source paper "fast responsive decentralised graph colouring"):
  with members:
    - "decentralized constraint satisfaction"
    - "decentralised learning macs for collision free access in wlans"
    - "towards a collision free wlan dynamic parameter adjustment in csma e2ca"

Connected through hyperedge HE-7 (induced by source paper "updating neighbour cell list via crowdsourced user reports a framework for measuring time performance"):
  with members:
    - "decentralized constraint satisfaction"

Connected through hyperedge HE-8 (induced by source paper "future evolution of csma protocols for the ieee 802 11 standard"):
  with members:
    - "decentralized constraint satisfaction"
    - "decentralised learning macs for collision free access in wlans"
    - "on the distributed construction of a collision free schedule in multi hop packet radio networks"
    - "modelling a decentralized constraint satisfaction solver for collision free channel access"
    - "towards a collision free wlan dynamic parameter adjustment in csma e2ca"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

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.NI(Networking and Internet Architecture)