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: "learning heuristics over large graphs via deep reinforcement learning"
  Abstract: Combinatorial optimization problems on graphs are routinely solved in various domains. Recently, it has been shown that heuristics for solving combinatorial problems can be learned using a machine learning-based approach. While existing techniques have primarily focussed on obtaining high-quality solutions, the aspect of scalability to billion-sized graphs has not been adequately addressed. In this paper, we propose a deep reinforcement learning framework called GCOMB to learn algorithms that can solve combinatorial problems over graphs at scale. Besides considering the traditional NP-hard combinatorial problems, we apply our framework to the popular and challenging data mining problem of Influence Maximization. GCOMB utilizes Graph Convolutional Network (GCN) to generate node embeddings that predict potential solution nodes. These embeddings are next fed to a Q-learning framework, which learns the combinatorial nature of the problem and predicts the final solution set. Through extensive evaluation on several synthetic and billion-sized real networks, we establish that GCOMB is more than 100 times faster than the state of the art while retaining the same quality of the solution sets.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning enhanced quantum inspired algorithm for combinatorial optimization"):
  with members:
    - "solving np hard problems on graphs by reinforcement learning without domain knowledge"
    - "exploratory combinatorial optimization with reinforcement learning"
    - "learning to optimize variational quantum circuits to solve combinatorial problems"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "learning to perform local rewriting for combinatorial optimization"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "device placement optimization with reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-2 (induced by source paper "a deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning fine grained search space pruning and heuristics for combinatorial optimization"):
  with members:
    - "learning multi stage sparsification for maximum clique enumeration"
    - "fine grained search space classification for hard enumeration variants of subset problems"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "fast algorithms for the maximum clique problem on massive graphs with applications to overlapping community detection"
    - "scalability of using restricted boltzmann machines for combinatorial optimization"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-4 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "graph policy gradients for large scale robot control"
    - "representation learning on graphs a reinforcement learning application"
    - "deep slam object level rgbd slam based on class specific deep shape priors"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "fusion volumetric object level slam"

Connected through hyperedge HE-5 (induced by source paper "an efficient graph convolutional network technique for the travelling salesman problem"):
  with members:
    - "graph2seq scalable learning dynamics for graphs"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning the multiple traveling salesmen problem with permutation invariant pooling networks"
    - "residual gated graph convnets"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "geometric deep learning going beyond euclidean data"
    - "learning combinatorial optimization algorithms over graphs"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for task driven discovery of incomplete networks"):
  with members:
    - "partitioning graphs for the cloud using reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep learning on graphs a survey"
    - "gemsec graph embedding with self clustering"
    - "selective harvesting over networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "emergence of locomotion behaviours in rich environments"
    - "block models and personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "exploratory combinatorial optimization with reinforcement learning"):
  with members:
    - "solving np hard problems on graphs by reinforcement learning without domain knowledge"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

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.LG(Machine Learning)