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: "density estimation for geolocation via convolutional mixture density network"
  Abstract: Nowadays, geographic information related to Twitter is crucially important for fine-grained applications. However, the amount of geographic information avail- able on Twitter is low, which makes the pursuit of many applications challenging. Under such circumstances, estimating the location of a tweet is an important goal of the study. Unlike most previous studies that estimate the pre-defined district as the classification task, this study employs a probability distribution to represent richer information of the tweet, not only the location but also its ambiguity. To realize this modeling, we propose the convolutional mixture density network (CMDN), which uses text data to estimate the mixture model parameters. Experimentally obtained results reveal that CMDN achieved the highest prediction performance among the method for predicting the exact coordinates. It also provides a quantitative representation of the location ambiguity for each tweet that properly works for extracting the reliable location estimations.

Connected through hyperedge HE-1 (induced by source paper "a hierarchical mixture density network"):
  with members:
    - "pedestrian prediction by planning using deep neural networks"
    - "recurrent mixture density network for spatiotemporal visual attention"

Connected through hyperedge HE-2 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "a survey of point of interest recommendation in location based social networks"
    - "exploiting text and network context for geolocation of social media users"
    - "analysis of named entity recognition and linking for tweets"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-3 (induced by source paper "semi supervised user geolocation via graph convolutional networks"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "multiview deep learning for predicting twitter users location"
    - "exploiting text and network context for geolocation of social media users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-4 (induced by source paper "continuous representation of location for geolocation and lexical dialectology using mixture density networks"):
  with members:
    - "estimating local commuting patterns from geolocated twitter data"
    - "twitter as a source of global mobility patterns for social good"
    - "exploiting text and network context for geolocation of social media users"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)