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: "what is tumblr a statistical overview and comparison"
  Abstract: Tumblr, as one of the most popular microblogging platforms, has gained momentum recently. It is reported to have 166.4 millions of users and 73.4 billions of posts by January 2014. While many articles about Tumblr have been published in major press, there is not much scholar work so far. In this paper, we provide some pioneer analysis on Tumblr from a variety of aspects. We study the social network structure among Tumblr users, analyze its user generated content, and describe reblogging patterns to analyze its user behavior. We aim to provide a comprehensive statistical overview of Tumblr and compare it with other popular social services, including blogosphere, Twitter and Facebook, in answering a couple of key questions: What is Tumblr? How is Tumblr different from other social media networks? In short, we find Tumblr has more rich content than other microblogging platforms, and it contains hybrid characteristics of social networking, traditional blogosphere, and social media. This work serves as an early snapshot of Tumblr that later work can leverage.

Connected through hyperedge HE-1 (induced by source paper "spread of hate speech in online social media"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "the effect of extremist violence on hateful speech online"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "hierarchical cvae for fine grained hate speech classification"
    - "thou shalt not hate countering online hate speech"
    - "hateminers detecting hate speech against women"
    - "analyzing right wing youtube channels hate violence and discrimination"

Connected through hyperedge HE-2 (induced by source paper "understanding and predicting delay in reciprocal relations"):
  with members:
    - "attributed network embedding for learning in a dynamic environment"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "network lasso clustering and optimization in large graphs"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "nonparametric link prediction in dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "doctag2vec an embedding based multi label learning approach for document tagging"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "neural architectures for named entity recognition"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "distributed representations of sentences and documents"
    - "lexicon infused phrase embeddings for named entity resolution"

Connected through hyperedge HE-4 (induced by source paper "evolution of ego networks in social media with link recommendations"):
  with members:
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "gender and interest targeting for sponsored post advertising at tumblr"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "estimating the causal impact of recommendation systems from observational data"
    - "structural properties of ego networks"
    - "discovering social circles in ego networks"
    - "the anatomy of the facebook social graph"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-5 (induced by source paper "spider and the flies focused crawling on tumblr to detect hate promoting communities"):
  with members:
    - "applying social media intelligence for predicting and identifying on line radicalization and civil unrest oriented threats"
    - "uncovering the wider structure of extreme right communities spanning popular online networks"

Connected through hyperedge HE-6 (induced by source paper "the apps you use bring the blogs to follow"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "gender and interest targeting for sponsored post advertising at tumblr"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "know your personalization learning topic level personalization in online services"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-8 (induced by source paper "a network aware approach for searching as you type in social media extended version"):
  with members:
    - "optimal aggregation algorithms for middleware"

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)