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: "distributed user profiling via spectral methods"
  Abstract: User profiling is a useful primitive for constructing personalised services, such as content recommendation. In the present paper we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local information exchanges between users. We compute a profile vector for each user (i.e., a low-dimensional vector that characterises her taste) via spectral transformation of observed user-produced ratings for items. Our two main contributions follow: i) We consider a low-rank probabilistic model of user taste. More specifically, we consider that users and items are partitioned in a constant number of classes, such that users and items within the same class are statistically identical. We prove that without prior knowledge of the compositions of the classes, based solely on few random observed ratings (namely $O(N\log N)$ such ratings for $N$ users), we can predict user preference with high probability for unrated items by running a local vote among users with similar profile vectors. In addition, we provide empirical evaluations characterising the way in which spectral profiling performance depends on the dimension of the profile space. Such evaluations are performed on a data set of real user ratings provided by Netflix. ii) We develop distributed algorithms which provably achieve an embedding of users into a low-dimensional space, based on spectral transformation. These involve simple message passing among users, and provably converge to the desired embedding. Our method essentially relies on a novel combination of gossiping and the algorithm proposed by Oja and Karhunen.

Connected through hyperedge HE-1 (induced by source paper "hidden hamiltonian cycle recovery via linear programming"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "sum of squares lower bounds for planted clique"
    - "exact recovery in the stochastic block model"
    - "information theory of dna shotgun sequencing"
    - "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"

Connected through hyperedge HE-2 (induced by source paper "distributed on line multidimensional scaling for self localization in wireless sensor networks"):
  with members:
    - "success and failure of adaptation diffusion algorithms for consensus in multi agent networks"
    - "distributed maximum likelihood sensor network localization"
    - "non line of sight node localization based on semi definite programming in wireless sensor networks"

Connected through hyperedge HE-3 (induced by source paper "parallel and distributed collaborative filtering a survey"):
  with members:
    - "factorbird a parameter server approach to distributed matrix factorization"
    - "gapfm optimal top n recommendations for graded relevance domains"
    - "mli an api for distributed machine learning"
    - "tag aware recommender systems a state of the art survey"
    - "efficient multicore collaborative filtering"
    - "a survey paper on recommender systems"
    - "solving k nearest neighbor problem on multiple graphics processors"
    - "the impact of social networks on multi agent recommender systems"

Connected through hyperedge HE-4 (induced by source paper "haplotype assembly an information theoretic view"):
  with members:
    - "low rank matrix recovery from errors and erasures"
    - "information theoretic bounds for low rank matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "the price of privacy in untrusted recommendation engines"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "privately releasing conjunctions and the statistical query barrier"
    - "matrix completion from noisy entries"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "differentially private empirical risk minimization"
    - "what can we learn privately"
    - "how to break anonymity of the netflix prize dataset"

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.LG(Machine Learning)