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: "loopy belief propagation as a basis for communication in sensor networks"
  Abstract: Sensor networks are an exciting new kind of computer system. Consisting of a large number of tiny, cheap computational devices physically distributed in an environment, they gather and process data about the environment in real time. One of the central questions in sensor networks is what to do with the data, i.e., how to reason with it and how to communicate it. This paper argues that the lessons of the UAI community, in particular that one should produce and communicate beliefs rather than raw sensor values, are highly relevant to sensor networks. We contend that loopy belief propagation is particularly well suited to communicating beliefs in sensor networks, due to its compact implementation and distributed nature. We investigate the ability of loopy belief propagation to function under the stressful conditions likely to prevail in sensor networks. Our experiments show that it performs well and degrades gracefully. It converges to appropriate beliefs even in highly asynchronous settings where some nodes communicate far less frequently than others; it continues to function if some nodes fail to participate in the propagation process; and it can track changes in the environment that occur while beliefs are propagating. As a result, we believe that sensor networks present an important application opportunity for UAI.

Connected through hyperedge HE-1 (induced by source paper "deep coordination graphs"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "the starcraft multi agent challenge"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "exploration with unreliable intrinsic reward in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "multiagent soft q learning"

Connected through hyperedge HE-2 (induced by source paper "distributed machine learning in materials that couple sensing actuation computation and communication"):
  with members:
    - "comparative study of deep learning software frameworks"
    - "deep activity recognition models with triaxial accelerometers"

Connected through hyperedge HE-3 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-4 (induced by source paper "asynchronous dynamic bayesian networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "continuous time bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "belief consensus algorithms for fast distributed target tracking in wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "geographic gossip efficient averaging for sensor networks"

Connected through hyperedge HE-6 (induced by source paper "performance of ldpc codes under faulty iterative decoding"):
  with members:
    - "the price of certainty waterslide curves and the gap to capacity"
    - "channel coding the road to channel capacity"
    - "the extraction and complexity limits of graphical models for linear codes"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "density evolution thresholds and the stability condition for non binary ldpc codes"

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)