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: "evolution of social attribute networks measurements modeling and implications using google"
  Abstract: Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the social network structure with user attributes (e.g., location, employer, communities of interest) can provide a more fine-grained understanding of social networks. However, there have been few studies to provide a systematic understanding of these effects at scale. We bridge this gap using a unique dataset collected as the Google+ social network grew over time since its release in late June 2011. We observe novel phenomena with respect to both standard social network metrics and new attribute-related metrics (that we define). We also observe interesting evolutionary patterns as Google+ went from a bootstrap phase to a steady invitation-only stage before a public release. Based on our empirical observations, we develop a new generative model to jointly reproduce the social structure and the node attributes. Using theoretical analysis and empirical evaluations, we show that our model can accurately reproduce the social and attribute structure of real social networks. We also demonstrate that our model provides more accurate predictions for practical application contexts.

Connected through hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-2 (induced by source paper "the art of social bots a review and a refined taxonomy"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "false information on web and social media a survey"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "detecting influence campaigns in social networks using the ising model"
    - "botnet campaign detection on twitter"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-3 (induced by source paper "personalized degrees effects on link formation in dynamic networks from an egocentric perspective"):
  with members:
    - "link prediction with node clustering coefficient"
    - "evaluating link prediction methods"
    - "social dynamics of digg"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "graph based security and privacy analytics via collective classification with joint weight learning and propagation"):
  with members:
    - "structure based sybil detection in social networks via local rule based propagation"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "node2vec scalable feature learning for networks"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "deepwalk online learning of social representations"
    - "detection of early stage enterprise infection by mining large scale log data"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"

Connected through hyperedge HE-5 (induced by source paper "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "practical locally private heavy hitters"
    - "locally differentially private heavy hitter identification"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"

Connected through hyperedge HE-6 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  with members:
    - "partitionedvc partitioned external memory graph analytics framework for ssds"
    - "graphit a high performance dsl for graph analytics"
    - "accelerating pagerank using partition centric processing"
    - "parallel local graph clustering"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "sybilfuse combining local attributes with global structure to perform robust sybil detection"):
  with members:
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "loopy belief propagation for approximate inference an empirical study"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-8 (induced by source paper "the structure and evolution of an offline peer to peer financial network"):
  with members:
    - "mind your credit assessing the health of the ripple credit network"
    - "four degrees of separation"

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)