human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the power of side information in subgraph detection"):
  Abstract: In this work, we tackle the problem of hidden community detection. We consider Belief Propagation (BP) applied to the problem of detecting a hidden Erdős-Renyi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when an effective signal-to-noise ratio (SNR) parameter falls below a threshold. In contrast, in the presence of non-trivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of the SNR parameter. We validate our results through simulations on synthetic datasets as well as on a few real world networks.

Includes members:
  Member "local set based graph signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "signal recovery on graphs variation minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering")
  Member "phase transitions in semisupervised clustering of sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "phase transitions in spectral community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "bayesian discovery of threat networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "influence estimation on social media networks using causal inference")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "generalized optimization framework for graph based semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "certifiable robustness to graph perturbations")

Question: Please tell me which class the source paper 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.LG(Machine Learning)