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 "community recovery in hypergraphs"):
  Abstract: Community recovery is a central problem that arises in a wide variety of applications such as network clustering, motion segmentation, face clustering and protein complex detection. The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each of which is associated with the values of a certain number of data points. While most of the prior works focus on a setting in which the number of data points involved in a measurement is two, this work explores a generalized setting in which the number can be more than two. Motivated by applications particularly in machine learning and channel coding, we consider two types of measurements: (1) homogeneity measurement which indicates whether or not the associated data points belong to the same community; (2) parity measurement which denotes the modulo-2 sum of the values of the data points. Such measurements are possibly corrupted by Bernoulli noise. We characterize the fundamental limits on the number of measurements required to reconstruct the communities for the considered models.

Includes members:
  Member "fast algorithms for robust pca via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")
  Member "community recovery in graphs with locality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "uniform hypergraph partitioning provable tensor methods and sampling techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction")
  Member "clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable k means and k medians clustering")
  Member "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "spectral detection on sparse hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs")

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.IT(Information Theory)