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: "active learning for node classification in assortative and disassortative networks"
  Abstract: In many real-world networks, nodes have class labels, attributes, or variables that affect the network's topology. If the topology of the network is known but the labels of the nodes are hidden, we would like to select a small subset of nodes such that, if we knew their labels, we could accurately predict the labels of all the other nodes. We develop an active learning algorithm for this problem which uses information-theoretic techniques to choose which nodes to explore. We test our algorithm on networks from three different domains: a social network, a network of English words that appear adjacently in a novel, and a marine food web. Our algorithm makes no initial assumptions about how the groups connect, and performs well even when faced with quite general types of network structure. In particular, we do not assume that nodes of the same class are more likely to be connected to each other---only that they connect to the rest of the network in similar ways.

Connected through hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "localization and centrality in networks"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff"):
  with members:
    - "graph powering and spectral robustness"
    - "a survey on theoretical advances of community detection in networks"
    - "connectivity in random annulus graphs and the geometric block model"
    - "active community detection a maximum likelihood approach"
    - "active learning for community detection in stochastic block models"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-4 (induced by source paper "active learning for graph neural networks via node feature propagation"):
  with members:
    - "simplifying graph convolutional networks"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "deep active learning for named entity recognition"
    - "active learning for graph embedding"
    - "neural message passing for quantum chemistry"
    - "a meta learning approach to one step active learning"

Connected through hyperedge HE-5 (induced by source paper "active community detection a maximum likelihood approach"):
  with members:
    - "active learning for community detection in stochastic block models"
    - "exact recovery in the stochastic block model"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "social structure of facebook networks"

Connected through hyperedge HE-6 (induced by source paper "structure and inference in annotated networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "community structure detection in complex networks with partial background information"
    - "community detection in networks with node attributes"
    - "friendship networks and social status"
    - "social structure of facebook networks"

Connected through hyperedge HE-7 (induced by source paper "model selection for degree corrected block models"):
  with members:
    - "oriented and degree generated block models generating and inferring communities with inhomogeneous degree distributions"

Connected through hyperedge HE-8 (induced by source paper "scalable text and link analysis with mixed topic link models"):
  with members:
    - "latent topic models for hypertext"
    - "a bayesian framework for community detection integrating content and link"
    - "latent multi group membership graph model"
    - "efficient and principled method for detecting communities in networks"

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.IT(Information Theory)