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: "on learning with finite memory"
  Abstract: We consider an infinite collection of agents who make decisions, sequentially, about an unknown underlying binary state of the world. Each agent, prior to making a decision, receives an independent private signal whose distribution depends on the state of the world. Moreover, each agent also observes the decisions of its last K immediate predecessors. We study conditions under which the agent decisions converge to the correct value of the underlying state. We focus on the case where the private signals have bounded information content and investigate whether learning is possible, that is, whether there exist decision rules for the different agents that result in the convergence of their sequence of individual decisions to the correct state of the world. We first consider learning in the almost sure sense and show that it is impossible, for any value of K. We then explore the possibility of convergence in probability of the decisions to the correct state. Here, a distinction arises: if K equals 1, learning in probability is impossible under any decision rule, while for K greater or equal to 2, we design a decision rule that achieves it. We finally consider a new model, involving forward looking strategic agents, each of which maximizes the discounted sum (over all agents) of the probabilities of a correct decision. (The case, studied in previous literature, of myopic agents who maximize the probability of their own decision being correct is an extreme special case.) We show that for any value of K, for any equilibrium of the associated Bayesian game, and under the assumption that each private signal has bounded information content, learning in probability fails to obtain.

Connected through hyperedge HE-1 (induced by source paper "social learning with questions"):
  with members:
    - "informational braess paradox the effect of information on traffic congestion"
    - "social learning with endogenous network formation"
    - "when is a network epidemic hard to eliminate"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "strategic learning and the topology of social networks"

Connected through hyperedge HE-2 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things"):
  with members:
    - "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
    - "label less learning for traffic control in an edge network"
    - "lte lte a random access for massive machine type communications in smart cities"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "learning with finite memory for machine type communication"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "reliable reporting for massive m2m communications with periodic resource pooling"

Connected through hyperedge HE-3 (induced by source paper "a graphical evolutionary game approach to social learning"):
  with members:
    - "bayesian quadratic network game filters"
    - "graphical evolutionary game for information diffusion over social networks"
    - "evolutionary dynamics of information diffusion over social networks"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "learning how to communicate in the internet of things finite resources and heterogeneity"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "learning with finite memory for machine type communication"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "mobile internet of things can uavs provide an energy efficient mobile architecture"
    - "towards massive ultra reliable and low latency wireless communication with short packets"
    - "wireless communications in the era of big data"
    - "towards massive machine type cellular communications"
    - "towards a consumer centric grid a behavioral perspective"

Connected through hyperedge HE-5 (induced by source paper "learning with finite memory for machine type communication"):
  with members:
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "power efficient system design for cellular based machine to machine communications"

Connected through hyperedge HE-6 (induced by source paper "whose opinion to follow in multihypothesis social learning a large deviations perspective"):
  with members:
    - "identifying infection sources and regions in large networks"
    - "controlled sensing for multihypothesis testing"
    - "the value of feedback in decentralized detection"
    - "error probability bounds for balanced binary relay trees"
    - "data fusion trees for detection does architecture matter"

Connected through hyperedge HE-7 (induced by source paper "finite time analysis of the distributed detection problem"):
  with members:
    - "switching to learn"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed detection over noisy networks large deviations analysis"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-8 (induced by source paper "robust decentralized detection and social learning in tandem networks"):
  with members:
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "learning in hierarchical social networks"
    - "hypothesis testing in feedforward networks with broadcast failures"
    - "error probability bounds for balanced binary relay trees"

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.GT(Computer Science and Game Theory)