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: "do you like what i like similarity estimation in proximity based mobile social networks"
  Abstract: While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers meeting. On the example of similarity in musical taste, we develop a solution for the problem of similarity estimation in proximity-based mobile social networks. We show that a single exchange of a probabilistic data structure between two devices can closely estimate the similarity of two users - without the need to contact a third-party server. We introduce metrics for fast and space-efficient approximation of the Dice coefficient of two multisets - based on the comparison of two Counting Bloom Filters or two Count-Min Sketches. Our analysis shows that utilizing a single hash function minimizes the error when comparing these probabilistic data structures. The size that should be chosen for the data structure depends on the expected average number of unique input elements. Using real user data, we show that a Counting Bloom Filter with a single hash function and a length of 128 is sufficient to accurately estimate the similarity between two multisets representing the musical tastes of two users. Our approach is generalizable for any other similarity estimation of frequencies represented as multisets.

Connected through hyperedge HE-1 (induced by source paper "affinity a system for latent user similarity comparison on texting data"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "context data categories and privacy model for mobile data collection apps"
    - "towards psychometrics based friend recommendations in social networking services"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "creating a live public short message service corpus the nus sms corpus"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-2 (induced by source paper "on gossip based information dissemination in pervasive recommender systems"):
  with members:
    - "affinity a system for latent user similarity comparison on texting data"
    - "collaborating with users in proximity for decentralized mobile recommender systems"
    - "context data categories and privacy model for mobile data collection apps"
    - "federated meta learning for recommendation"
    - "federated meta learning with fast convergence and efficient communication"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-3 (induced by source paper "collaborating with users in proximity for decentralized mobile recommender systems"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "context data categories and privacy model for mobile data collection apps"
    - "towards psychometrics based friend recommendations in social networking services"
    - "communication efficient learning of deep networks from decentralized data"

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.SI(Social and Information Networks)